Academic Publications

EXPRESS: Sales Content Platforms and Opportunity Conversion

2025-12-25T00:00:00+00:00

Production and Operations Management
To improve sales and operational processes that support business outcomes, such as converting sales opportunities and the revenue from these conversions, firms invest in sales content platforms—shared digital warehouses for storing, managing, and distributing information required for sales activities. However, the effectiveness of such digital sales operations investments remains unclear. To investigate, this study explores the effects of salespeople accessing sales content platforms across two studies. First, a qualitative study reveals that access to sales content platforms helps salespeople learn valuable information and adapt their behaviors throughout the sales process. Salespeople with extensive tenure benefit disproportionately from sales content platform access, especially when it is unclear which information to rely on and how best to act on it. These insights inform the quantitative analysis in our second study, which utilizes a unique dataset from a global business-to-business firm specializing in advanced technologies. The results confirm that access to sales content platforms helps salespeople convert sales opportunities, but this effect depends on the type of content accessed, the sales process phase, and salesperson tenure. Furthermore, the benefits of sales content platform access do not always lead to increased revenue from converted opportunities. These findings offer actionable guidance for managers seeking to maximize returns on their investments in sales content platforms.

The Long‐Lasting Effects of Experiencing Communism on Attitudes toward Financial Markets

2025-12-25T00:00:00+00:00

The Journal of Finance
We show that exposure to anti‐capitalist ideology can exert a lasting influence on attitudes toward capital markets and stock market participation. Using novel survey, bank, and broker data, we document that, decades after Germany's reunification, East Germans invest significantly less in stocks and hold more negative views on capital markets. Effects vary by personal experience under communism. Results are strongest for individuals who remember life in the German Democratic Republic positively, for example, those living in a “showcase city.” Results reverse for those with negative experiences like environmental pollution or lack of Western TV entertainment.

When do firms learn by hiring? How complexity moderates the value of new knowledge

2025-12-26T00:00:00+00:00

Strategic Management Journal
Organizations often hire employees hoping to acquire new knowledge. While the literature has paid considerable attention to the role of the characteristics of the source of knowledge, the recipient firm, and the knowledge being transferred, it has largely overlooked those of the knowledge being replaced. Using a computational model, we examine how the pre‐existing knowledge of the hiring firm—specifically its degrees of internal and external fit—influences its ability to learn. Our findings suggest that firms with lower internal fit absorb new knowledge more quickly, even when controlling for initial external fit. We identify several mechanisms driving this dynamic, demonstrating how persistent resistance to new knowledge and sudden shifts can emerge solely through mutual learning dynamics between individuals and organizations, independent of social or cognitive constraints.Managerial SummaryCompanies frequently hire employees from competitors to gain new knowledge and improve performance. We show that success in learning by hiring depends not only on who firms hire but also on the characteristics of their existing knowledge. Our findings reveal two counterintuitive dynamics. First, firms whose practices exhibit a high degree of fit face greater difficulty in absorbing new knowledge. Such extant knowledge is stickier, as incumbent employees find it harder to abandon their old approaches and keep pulling the organization back to the status quo. Second, in complex environments, struggling firms that hire aggressively may learn less effectively, as multiple hires provide conflicting advice. Thus, while such firms stand to learn more from hiring, the internal dynamics of learning within the organization frustrate the firm's effort to absorb the knowledge. We subsequently present and analyze the mechanisms responsible for these outcomes.

Financial Restructuring and Resolution of Banks

2025-12-26T00:00:00+00:00

The Review of Financial Studies
How do resolution frameworks affect the private restructuring of distressed banks? We model a bank’s shareholders and creditors negotiating a restructuring, under two frictions: asymmetric information about asset quality and externalities on the government. High-quality banks signal themselves by delaying the negotiation, which is socially inefficient. Public policies can improve welfare if they reduce the signaling motive or increase the negotiation surplus. Stricter bail-in rules make debt more information sensitive and increase delays. The bank chooses a capital structure with too little renegotiable debt, giving a new rationale, for example, for Total Loss Absorbing Capacity requirements. (JEL G21, G28)

Mortgage Design, Repayment Schedules, and Household Borrowing

2025-12-26T00:00:00+00:00

The Review of Financial Studies
How does the design of debt repayment schedules affect household borrowing? To answer this question, we exploit a Swedish policy reform that eliminated interest-only mortgages for loan-to-value ratios above 50%. We document substantial bunching at the threshold, leading to 5% lower borrowing. Wealthy borrowers drive the results, challenging credit constraints as the primary explanation. We develop a model to evaluate the mechanisms driving household behavior and find that much of the effect comes from households experiencing ongoing flow disutility to amortization payments. Our results indicate that mortgage contracts with low initial payments substantially increase household borrowing and lifetime interest costs. (JEL G51, G21, E21, E6)

Potty Parity

2025-12-26T00:00:00+00:00

Management Science
We address the issues of unequal restroom access for women and LGBTQ+ individuals, known as the potty parity problem. We propose a utility model in which users consider gender identity, wait time, and safety concerns when choosing restrooms. We evaluate different layouts’ efficiency measured by the total utilities (as in the utilitarian principle) and assess their fairness using the measures of the minimum utility gain (as in the Rawlsian fairness) and the gap between maximum and minimum gains (as in the distributive fairness). When the population is sensitive to gender identity and safety concerns, although it may initially seem intuitive to assume that converting all restrooms to unisex facilities would be efficient and fair due to the pooling of servers and increased flexibility and perceived fairness due to all users standing in the same line, our findings demonstrate that this design can be neither efficient nor fair. In contrast, we show that converting some men’s restrooms to unisex can enhance both efficiency and fairness of access. This highlights that a moderate level of flexibility can outperform a fully flexible system. Moreover, conventional wisdom suggests that removing a restroom unit from the men’s room would negatively impact users from the men’s side. However, our analysis reveals a counterintuitive insight that such a change can lead to a Pareto improvement, benefiting all users involved. We also analytically explore additional benefits of unisex restrooms under different user behaviors and situations and present practically relevant numerical results to support our findings.This paper was accepted by Elena Katok, operations management.Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2021-04295].Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2021.04075 .

Underperformance by Design: A Scoping Review and Research Agenda of Intentional Task Underperformance at Work

2025-12-26T00:00:00+00:00

Journal of Management
Recent workplace trends reveal that workers frequently discuss and engage in deliberate underperformance, underscoring the growing relevance of intentional underperformance at work. Although various disciplines have long studied purposeful reductions in work effort, these investigations have remained largely siloed, hindering conceptual clarity. In this scoping review, we integrate perspectives from management, social and educational psychology, and economics to establish a unified conceptualization of intentional underperformance (i.e., the deliberate suppression of task contributions, such that contributions fall below a relevant benchmark). In doing so, we make five primary contributions to the performance literature. First, we review and consolidate 36 related constructs to develop an umbrella conceptualization that can promote cumulative scientific understanding. Second, based on our review, we present an organized view of the antecedents and proximal motives of intentional underperformance. Third, we introduce the Intentional Underperformance Framework, a two-by-two that organizes forms of intentional underperformance, and organize the associated outcomes of these different forms. Fourth, we identify cross-disciplinary patterns in operationalizations, summarizing prevailing methods and their implications. Fifth, we outline a research agenda to guide future inquiry into when, why, and how intentional underperformance arises, as well as how organizations can constructively intervene. By integrating disconnected literatures and promoting a shared language, this review provides a foundation for more integrative theoretical and empirical inquiries. Ultimately, we aim to equip scholars and practitioners with a clearer, more comprehensive understanding of intentional underperformance in today’s workplace.

Beyond Blame: Evaluative Stigma, Attribution, and Employee Careers after Employer Failure

2025-12-26T00:00:00+00:00

Administrative Science Quarterly
Employment continuity facilitates access to career opportunities, professional growth, and financial security, making involuntary disruptions—such as employer failures—potentially consequential for individuals’ career trajectories. Although prior research has explored how organizational leaders’ careers fare following employer failure, the implications for non-executive employees remain unclear in theoretical terms. Integrating theories of evaluation, evaluative stigma, and careers, we develop a theoretical framework to clarify how employer failure relates to subsequent career outcomes for employees. Using confidential, anonymized data from the United States Census complemented by detailed, identifiable data from the Automatic Speech Recognition industry, we find that employer failure is negatively related to organizational leaders’ wage growth, consistent with internal attribution and evaluative stigma. Conversely, non-executive employees experience wage (and industry retention) outcomes comparable to those of their unaffected peers, suggesting that affiliation with a failed employer does not stigmatize this set of employees. However, non-executive employees are not always unscathed: Career penalties emerge when failure involves a scandal, when industry labor-market conditions become more competitive, and when employees belong to some marginalized demographic groups (gender, race, immigration). By clarifying the conditions of evaluative stigma, we extend organizational research on careers and evaluations, highlighting the conditions under which affiliations with failed firms relate to subsequent career outcomes.

AD Two Become One: Foreign Capital and Household Credit Expansion

2025-12-27T00:00:00+00:00

The Review of Financial Studies
Rapid credit expansions predict lower output growth and banking crises, but does it matter who finances them? We identify the ultimate counterparties financing credit expansions in a panel of 33 advanced economies and find that foreign-financed household credit expansions predict lower GDP growth and higher crisis risk, but domestically financed credit expansions do not. Studying the mechanisms, we find that foreign-financed household credit expansions are accompanied by higher supply of foreign capital (reflected in low credit spreads), are followed by elevated credit cycle reversal risk, and lead to higher debt service payments to foreigners which depress aggregate demand. (JEL: E32, E44, F34, G01, G15, G51)

Linguistic Capital, Social Capital, and Self-Employment of Rural-Urban Migrants in China

2025-12-27T00:00:00+00:00

Entrepreneurship Theory and Practice
The entrepreneurship literature has focused on the communicative function of language, but has rarely examined linguistic capital’s effects on entrepreneurship, particularly that of dialect similarity. This research explores the role of dialect similarity in the self-employment of rural-urban migrants and how social capital, including networks and trust, mediates the role of dialect similarity in a developing economy setting. Integrating the linguistic capital theory of French sociologist Bourdieu with the social capital theory, the study examines the decision of rural-urban migrants’ self-employment in China. Analyzing the dataset from the China Migrants Dynamic Survey, we argue that rural-urban migrants who speak a dialect similar to the place of residence have a higher likelihood of engaging in self-employment, and that social capital plays a positive mediating role in self-employment decisions. We employ the Instrumental Variable and Propensity Score Matching method with a quasi-experimental approach for our empirical test. Results from both approaches strongly support our hypotheses. Lastly, research contributions and implications are discussed.

When do nice guys finish last? Prosociality and the psychological model of CEO ‐firm matching

2025-12-28T00:00:00+00:00

Strategic Management Journal
Prosocial CEOs, characterized by greater concern for their employees, enhance employee motivation but incur higher costs when implementing layoffs. We develop a psychological model of CEO‐firm matching wherein negative industry shocks requiring downsizing asymmetrically erode the match quality for prosocial CEOs. Leveraging increases in Chinese import competition, we show that layoff pressures lead to higher rates of both forced and voluntary turnover among prosocial CEOs. They are succeeded by less prosocial CEOs who are externally recruited, use less employee‐friendly language, lean Republican in political orientation, or are less likely to volunteer at charities. Our study highlights psychological characteristics as a key consideration in the executive labor market and draws attention to the “first‐stage” selection dynamics that shape the types of CEOs who lead firms.Managerial SummaryWhich firms CEOs choose to join and which CEOs are selected or retained by boards depend not only on their skills but also on the fit between their ‘personality’ and the firm's needs. We show that during industry downturns that require aggressive downsizing, prosocial CEOs are more likely to depart voluntarily, and boards actively replace them with low‐prosocial “wartime” CEOs. This dynamic nature of the CEO‐firm fit provides insights into why and when previously effective leadership may become ineffective and empirical grounds for the common distinction between peacetime and wartime CEOs. A key implication is that increasing Chinese import competition has shaped not only firm economic activities but also the psychological profiles of business leaders.

The Intangibles Song in Takeover Announcements: Good Tempo, Hollow Tune

2025-12-29T00:00:00+00:00

The Review of Financial Studies
Mergers and acquisitions are often motivated by an intention to create value from intangible assets. We develop a word list of intangibles and apply it to takeover announcements. One standard deviation more in intangible-related language (“intangibles talk”) lowers announcement returns for the acquirer by 0.53 percentage points and predicts worse operating performance. Bidder managers appear to believe in the deals nonetheless, as evidenced by insider trades, payment choices, and completion probabilities and speed. Overall, takeover announcement texts reveal important information about hard-to-measure aspects of deal quality. (JEL G14, G34, G41)

The Economics of Equilibrium with Indivisible Goods

2025-12-29T00:00:00+00:00

Review of Economic Studies
This paper develops a theory of competitive equilibrium with indivisible goods based entirely on economic conditions on demand. The key idea is to analyze complementarity and substitutability between bundles of goods, rather than merely between goods themselves. This approach allows us to formulate sufficient and essentially necessary conditions for equilibrium existence—which unify settings with complements and settings with substitutes. Our analysis has implications for auction design.

EXPRESS: Concentration in Academic Publishing and Journal Impact: A Descriptive Assessment of Top 46 Business Journals

2025-12-29T00:00:00+00:00

Production and Operations Management
This study assesses concentration of the top 50 universities (T50 concentration) in premier business journals and its correlation with journal impact metrics. We use 2008-2022 data from the Web of Science and Clarivate Analytics, focusing on 46 business journals from the University of Texas at Dallas (UTD 24) and Financial Times (FT 50) lists to document several findings. First, we find that the top 50 institutions, representing less than 0.5% of business schools globally, contribute a disproportionately large share of about 53% publications in these outlets. Second, our longitudinal analyses indicate that T50 concentration has remained relatively stable over the 15-year period. Finally, we find evidence of a positive and statistically significant association between T50 concentration and Article Influence Score, although we do not find any statistically significant relationship with other journal impact metrics. Importantly, we find a decline in the publication and citation share of articles authored exclusively by top-50 author teams, and a rise in share for mixed author teams from top-50 and non-top-50 universities. These findings provide data-driven insights to inform debates relating to merit and elitism in editorial and review processes in top journals.

On the Impossibility of Statistically Improving Empirical Optimization: A Second Order Stochastic Dominance Perspective

2025-12-29T00:00:00+00:00

Management Science
When the underlying probability distribution in a stochastic optimization is observed only through data, various data-driven formulations have been studied to obtain approximate optimal solutions. We show that no such formulations can, in a sense, theoretically improve the statistical quality of the solution obtained from empirical optimization. We argue this by proving that the first order behavior of the optimality gap against the oracle best solution, which includes both the bias and variance, for any data-driven solution second order stochastically dominates that from empirical optimization as long as suitable smoothness holds with respect to the underlying distribution. We demonstrate this impossibility of improvement in examples ranging across regularized optimization, distributionally robust optimization, parametric optimization, and Bayesian generalizations. We also discuss the connections of our results to other perspectives in statistics and data-driven optimization and illustrate practical implications in choosing among data-driven formulations.This paper was accepted by J. George Shanthikumar, data science.Funding: This work was supported by the National Science Foundation Division of Information and Intelligent Systems [Grant 1849280] and Division of Civil, Mechanical, and Manufacturing Innovation [Grant 1834710].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.04482 .

Risk Sharing in Government Contracting: Strategic Alliances as Safeguards in Government Supplier Relationships

2025-12-29T00:00:00+00:00

Journal of Management
Interorganizational alliances have been extensively studied as strategic arrangements that enable firms to manage risks arising from their embeddedness in external relationships. However, the unique dynamics of business-to-government (B2G) relationships, where firms often face regulatory and political risks, remain underexplored. In this study, we examine how firms reliant on U.S. Department of Defense (DoD) contracts use strategic alliances to mitigate these challenges. Drawing on resource dependence theory and the resource-based view of the firm, we theorize that firms with higher government contract value are more likely to form alliances with other government contractors to help share risk and navigate government contracting challenges. We further identify two boundary conditions—an internal buffer (whether a firm operates as a generalist or specialist contractor) and an external buffer (the level of market-demand risk)—that moderate this relationship. Our analysis of 339 U.S. publicly traded DoD contractors from 2001 to 2019 provides robust support for our hypotheses. A post hoc mediation analysis further shows that alliances partially mediate the relationship between government contract value and market performance. Our study contributes to interorganizational relationships and business-government interface literatures by articulating the unique dynamics of B2G alliances and offering nuanced insights into how firms manage their relationships with powerful government buyers.

Does a Long-Term Orientation Lead to Faster Growth? Firms’ Temporal Orientation and the Growth Process

2025-12-29T00:00:00+00:00

Entrepreneurship Theory and Practice
This article investigates how firms’ temporal orientation—operationalized through their investment horizon—influences the growth process. We propose that longer investment horizons reflect a forward-looking orientation that facilitates resource accumulation and capability development, ultimately supporting firm expansion. However, we also argue that excessively long horizons may lead to diminishing returns due to organizational frictions and coordination constraints. We find an inverted U-shaped relationship between investment horizon and growth in fixed assets, employment, and sales. These findings underscore the complex role of temporal orientation in shaping distinct aspects of firm growth.

Shifts in Innovation Focus in Response to Online and Offline Shareholder Activism: Unpacking Patterns in Family and Non-Family Firms

2025-12-29T00:00:00+00:00

Entrepreneurship Theory and Practice
This study investigates how online and offline shareholder activism influences a firm’s innovation focus—on exploration or exploitation—in family and non-family firms. We theorize, from a mixed gamble perspective, that such activisms pose distinctive threats to a firm’s economic and non-economic interests. We analyze 7,887 firm-year observations from Chinese listed firms (2012–2022) and find that non-family firms are more likely than family firms to shift toward exploitation in response to offline activism, and family firms are more inclined to focus on exploration when facing online activism. Additionally, second-generation family firms are found to be more responsive than first-generation firms to both forms of shareholder activism.

Entrepreneurship and Physical Health: A Natural Experiment Based on the 1956 British Clean Air Act

2025-12-29T00:00:00+00:00

Entrepreneurship Theory and Practice
I investigate whether there is a causal relationship between physical health and entrepreneurship by exploiting the exogenous introduction of the British Clean Air Act (CAA). This legislation generated across-cohort differences in exposure to airborne pollution. A fuzzy regression discontinuity design is used to investigate the causal impact of improved health following the CAA on adult entrepreneurship outcomes. The results carry important implications for scholarship as well as policymakers, with many governments in emerging economies seeking to curb airborne pollution and improve health and economic outcomes for their citizens, while also seeking to promote entrepreneurship.

A Robust Test for Weak Instruments for 2SLS with Multiple Endogenous Regressors

2025-12-30T00:00:00+00:00

Review of Economic Studies
We develop a test for instrument strength based on the bias of two-stage least squares (2SLS) that (1) generalizes the tests of Stock and Yogo (2005) and Sanderson and Windmeijer (2016) to be robust to heteroskedasticity and autocorrelation, and (2) extends the Montiel Olea and Pflueger (2013) robust test for models with a single endogenous regressor to multiple endogenous regressors. Our test can be based either on Stock and Yogo’s (2005) absolute bias criterion or on the 2SLS bias relative to Montiel Olea and Pflueger’s (2013) worst-case benchmark. We also develop extensions to test whether weak instruments cause bias in individual 2SLS coefficients. In simulations, our test controls size and is powerful, and we provide efficient code packages for its practical implementation. We demonstrate our testing procedures in the context of the estimation of state-dependent fiscal multipliers as in Ramey and Zubairy (2018).

Markups Across Space and Time

2025-12-30T00:00:00+00:00

Review of Economic Studies
In this article, we provide direct evidence on the behaviour of markups in the retail sector across space and time. Markups are measured using gross margins. We consider three levels of aggregation: the retail sector as a whole, the firm, and the product level. We find that: (1) markups are relatively stable over time and mildly procyclical; (2) there is a large regional dispersion in markups; (3) there is a positive cross-sectional correlation between local income and local markups; and (4) differences in markups across regions result from differences in the assortment of goods sold in different regions, not from deviations from uniform pricing. We propose a simple model consistent with these facts.

When Interpretations of Merit Thresholds Vary and Reproduce Inequality: Entering the Tech Industry Without Computer Science Credentials

2025-12-30T00:00:00+00:00

Organization Science
Meritocracy is widely believed to be a fair system. Although extant literature focuses on managers’ implementation of meritocratic decisions, less attention has been paid to jobseekers’ responses to meritocratic opportunities. This study addresses this gap by examining how aspiring software developers without computer science (CS) degrees respond to the ostensibly meritocratic promise of entering tech through open-access coding skills. Drawing on interview, social media, and ethnographic data, I find that although all aspirants agreed coding skills were the key meritocratic criteria for entering tech without CS degrees, they interpreted the merit threshold (the level of coding competency needed to get their first job) differently and adopted three distinct entry strategies that varied in timing and scope—Early/Broad, Standard, and Late/Narrow. Follow-up data collected three years later revealed that one strategy (Early/Broad) was associated with high employment rates across all subgroups of aspirants and substantially increased employment chances for those historically underrepresented in tech. Yet, most did not opt for it. Indeed, only one group (White men with white-collar/professional backgrounds) clustered in strategies with higher employment rates, resulting in this population securing jobs at a higher rate than others. To explain the variation in merit thresholds and accompanying entry strategies, this study highlights aspirants’ previous encounters with demand-side actors—particularly, their perceptions of whether, and to what extent, employers had previously been willing to give them a chance. These findings contribute to research on meritocracy and labor markets and offer insights into building a more diverse workforce.Funding: This work was supported by the American Sociological Association Doctoral Dissertation Research Improvement [Grant 55209764] and the Washington Center for Equitable Growth [Grant 5510223].Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.17752 .

Matching Drivers to Riders: A Two-Stage Robust Approach

2025-12-30T00:00:00+00:00

Operations Research
Smarter Matchmaking for Ride-Hailing PlatformsRide-hailing platforms such as Uber, Lyft, and DiDi must assign drivers to riders every minute without knowing who will request a ride next. Most systems optimize each batch myopically, and this can leave some future riders waiting much longer than necessary. In “Matching Drivers to Riders: A Two-Stage Robust Approach,” accepted for publication in Operations Research, the authors propose a two-stage robust matching model that incorporates uncertainty about future demand. The first stage matches current riders, reserving enough nearby drivers for plausible future scenarios; the second stage then tests these decisions against the most adverse demand patterns to guarantee good performance even in the worst case. The authors prove that finding the best such policy is computationally hard, but they develop constant factor approximation algorithms for several practically relevant cases and test them on large-scale taxi data from Shenzhen, China. Their methods substantially reduce maximum rider waiting times with little or no sacrifice in average travel distances.

Forecasted vs. Actual Generosity in Image-Concern Interventions

2025-12-30T00:00:00+00:00

Management Science
Prior research and lay intuition suggest that amplifying image concerns promotes generosity. We test the impact of image-based interventions in consumer elective pricing (CEP), where individuals choose how much to pay for products or services. Across 10 studies (nine field experiments and one laboratory study, n = 3,182), we examine the effects of these interventions on payments and elicit forecasts from independent participants (n = 1,636). Forecasters predict large positive effects, yet behavioral data reveal small and inconsistent impacts. What drives the inconsistency between predictions and actual outcomes? Studies with more than 4,000 forecasters show that neither simulating the decision-making experience nor prior experience with CEP improve prediction accuracy. However, avoiding direct comparisons between experimental conditions—which can artificially amplify perceived differences—reduces overestimation. These findings highlight challenges associated with transferring behavioral insights across contexts and show how forecast elicitation methods shape expectations of intervention effectiveness.This paper was accepted by Yan Chen, behavioral economics and decision analysis.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00149 .

Missing Team Dynamics? An Integrative Review of Research on Team Development Over Time

2025-12-30T00:00:00+00:00

Journal of Management
Theory describing the development, functioning, and performance of work teams emphasizes their dynamic nature. For many years, empirical research on teams has failed to keep pace with theory, impeding understanding of how teamwork unfolds over time and teams as a whole. However, over the past several decades, research examining the onset and functioning of change in teams has significantly increased. Our review distinguishes two primary theoretical perspectives that have dominated the field’s understanding of team development and provides a foundation for integrating both perspectives, which would allow for a more nuanced understanding. With the overarching goal of summarizing findings from this burgeoning literature, we conduct a systematic review of 110 articles (116 studies) that examine team development and teams over time. We synthesize evidence to illustrate how team phenomena (i.e., composition, leadership, emergent states, processes, and outcomes) change over time and interact dynamically with one another. We conclude by offering several high-level critiques of the literature, along with proposed solutions to address these issues. In doing so, we move beyond a general call for more research on team dynamics toward a more precise account of the decisions and considerations that should guide future work. Taken together, our manuscript provides scholars with a detailed account of how teams develop over time, challenging the long-held assumption that there is a dearth of work in this domain, and a foundation to position future work within a more integrated understanding of team development.

The Glass Is Half Full: A Gendered Model of Illegal Entrepreneurship

2025-12-30T00:00:00+00:00

Journal of Management
Individuals in impoverished communities often face considerable adversity. Under such circumstances, they can turn to illegal entrepreneurship. However, from a gendered perspective, women are typically considered incongruent with the masculinity of crime and entrepreneurship and, thus, illegal entrepreneurship. In this study, we were interested in exploring how women navigate their society’s gender role expectations to engage in illegal entrepreneurship. We adopted a qualitative, inductive approach to explore the cognitive processes through which women entrepreneurs navigate these tensions to manufacture and sell illegal alcohol within their impoverished communities throughout India. Our resulting gendered model of the cognitive processes underlying illegal entrepreneurship in impoverished communities offers new insights into how women entrepreneurs use cognitive carve-outs to navigate potentially conflicting societal expectations regarding gender and entrepreneurial roles. Further, we explore how entrepreneurship is influenced by construals, particularly in contexts of resource scarcity and gendered constraints. Finally, in line with the dark side of entrepreneurship, we shed light on how women can justify to themselves and others entrepreneurial action that, while shielding themselves from immediate personal repercussions, imposes substantial costs on many members of their impoverished communities.

The Orchestrator’s Dilemma: How Ericsson Strategically Recombined Resource Commitments and Signaling Tactics to Outmaneuver WiMAX

2025-12-30T00:00:00+00:00

Journal of Management
Orchestrators shape ecosystem development by aligning diverse participants while advancing their own competitive agendas. This alignment becomes fragile when a new entrant offers an alternative value proposition that appeals to ecosystem participants but threatens an orchestrator’s position. Whereas prior research highlights the success of new entrants, less is known about how an orchestrator defends its competitive interests and position while addressing expectations of cooperation from ecosystem participants, which we call the “orchestrator’s dilemma.” We investigate these dynamics through a longitudinal study of Ericsson’s responses as Intel attempted to promote WiMAX as an alternative to LTE, Ericsson’s fourth-generation mobile technology. Our analysis shows how Ericsson recombined its substantive resource commitments and signaling tactics to develop three distinct strategic responses—avoiding competition, covert competition, and overt competition—as ecosystem support for the new entrant’s value proposition changed over time. We show how an orchestrator can defend its position against disruptive entrants by decoupling signaling tactics from substantive resource commitments to influence ecosystem dynamics. We contribute by theorizing these dynamic strategic responses as a way to overcome the orchestrator’s dilemma and by highlighting the social aspects of ecosystem alignment and its fragility.

Firm Growth and Corporate Social Responsibility

2025-12-31T00:00:00+00:00

Journal of Management
This paper examines how the rate of firm growth influences corporate social responsibility (CSR). Periods of rapid growth can place substantial financial and managerial demands on firms, requiring leaders to prioritize the allocation of limited resources toward managing expansion. We propose that when firms grow quickly, managers may limit CSR investments—not because they undervalue CSR, but because growth itself consumes financial and attentional capacity. Using 19 years of data from 4,305 firms across 46 countries, we find that faster firm growth is associated with lower levels of CSR activity. This relationship is moderated by firms’ financial and attentional resources: firms with greater financial slack and managerial bandwidth maintain stronger CSR engagement even while growing rapidly. Overall, our results suggest that the relationship between growth and CSR reflects a pragmatic balancing of competing demands.

New venture team stability and long‐run organizational growth

2026-01-14T16:08:19+00:00

Strategic Entrepreneurship Journal
We explore the impact of new venture team (NVT) stability on long‐run organizational growth. With an instrumental variable design, we leverage a matched employer‐employee dataset of all Danish new ventures from 1981 to 1997. We find strong evidence that NVT stability has a positive effect on organizational growth in employees and that the effect grows stronger over time. We also find that stability is especially impactful for larger teams and for teams with higher education levels. The gains from stability also appear to be driven entirely by mixed‐gender teams. We connect our findings to the literature on NVT dynamics and suggest avenues for future research.Managerial SummaryStability within founding teams is crucial for the longevity and expansion of new ventures. We examine a dataset of Danish startups and find that ventures with stable founding teams demonstrate a 16.1 percentage point higher likelihood of survival and a 20.4% increase in average size after 10 years. This effect is accentuated in larger, more educated, and gender‐diverse teams. For entrepreneurs, these insights underscore the importance of not only assembling a strong initial team but also maintaining its composition to leverage growth opportunities as the business evolves.

Dynamic Portfolio Allocation Under Market Incompleteness and Wealth Effects

2026-01-14T16:08:19+00:00

Operations Research
Dynamic Portfolio Allocation Under Market Incompleteness and Wealth EffectsIn the paper “Dynamic portfolio allocation under market incompleteness and wealth effects,” a novel decomposition of an optimal dynamic portfolio is developed under general incomplete-market models and the wealth-dependent hyperbolic absolute risk aversion (HARA) utility. It shows that with hedgeable interest rate risk, the optimal portfolio consists of two parts: a pure constant relative risk aversion optimal portfolio and a financing bond portfolio for investor future subsistence requirements. Under such a structure, the wealth growth rate is always higher for HARA investors with more initial wealth, leading to increased wealth inequality regardless of the underlying model dynamics and realized market scenario. Using the decomposition, the authors solve the HARA optimal policy in closed form under an incomplete-market model with both stochastic interest rate and volatility. The wealth effect in the optimal portfolio has interesting implications. It generates a procyclical pattern in investor stock positions and time-varying risk aversion levels as well as a “buy high, sell low” market timing effect that may hurt HARA investors with low initial wealth.

Technical Note—Fairness-Aware Online Price Discrimination with Nonparametric Demand Models

2026-01-14T16:08:19+00:00

Operations Research
Incorporating Fairness into Online Price DiscriminationIn the paper “Fairness-aware online price discrimination with nonparametric demand models,” Xi Chen, Jiameng Lyu, Xuan Zhang, and Yuan Zhou explore how fairness can be integrated into dynamic pricing strategies. The authors propose a model that enforces price fairness constraints, ensuring that price differences between customer groups remain within a specified range. Their approach introduces a novel regret lower bound, which contrasts with the typical regret seen in traditional pricing algorithms. This shift underscores the added complexity of optimizing revenue while maintaining fairness. The study not only advances the understanding of fairness-aware dynamic pricing but also enriches the dynamic pricing literature by offering new lower-bound techniques. These insights may be useful for deriving lower bounds in other problems related to learning optimal prices under constraints. Their work contributes significantly to the growing need for ethical pricing practices in data-driven markets.

Exponential Concentration in Stochastic Approximation

2026-01-14T16:08:19+00:00

Operations Research
We analyze the convergence rates of stochastic approximation algorithms under nonvanishing gradient conditions. For these sharp (V-shaped) functions, standard Gaussian approximations fail, making tighter exponential concentration bounds more suitable. We prove that stochastic approximation algorithms, including Projected Stochastic Gradient Descent (PSGD), Kiefer-Wolfowitz, and Frank-Wolfe algorithms, exhibit exponential concentration near an optimum. A consequence is faster convergence rates, notably linear convergence, and O(1/t) rates. The paper leverages techniques from Markov chain theory, specifically geometric ergodicity and Lyapunov drift analysis, thereby extending previous results by Hajek (1982) .

Performance of the Offer-Everything Policy

2026-01-14T16:08:19+00:00

Operations Research
Should the seller display all available products in a setting with multiple products or only offer a subset? Is there a benefit of withholding some products? The seller does not want to display low-margin products due to cannibalization, but what if all products have the same margin? In “Performance of the Offer-Everything Policy,” W. T. Huh, J. Paat, and M. Queyranne argue that in an environment where the seller cannot adjust assortment based on customer types, it is best to offer all available products in an environment. The analysis is based on competitive ratio and expected revenue. This research assures that a common practice of truthfully revealing product availability is a generally good policy to follow.

Optimal Allocation of Limited Inventory Among Multiclass Customers with Finite Populations

2026-01-14T16:08:19+00:00

Operations Research
New Analytical Insights on Inventory AllocationHow should a limited inventory of a single resource be allocated across multiple customer groups with distinct rewards and arrival rates, especially when each group has a finite population? In their paper “Optimal allocation of limited inventory among multiclass customers with finite populations” in Operations Research, the authors develop a stochastic framework and formulate the problem as a Markov decision process. By analyzing the structure of the optimal value function, they reveal a new insight; rather than gradually expanding access to lower-priority groups over time, it is in fact optimal to progressively restrict access to these groups. The paper also introduces a fluid model with an explicit solution, which provides a good approximation when the system size is large. These findings offer both theoretical and practical contributions to inventory allocation problems, with potential applications in healthcare and humanitarian resource management as well as commercial product sales.

Single-Leg Revenue Management with Advice

2026-01-14T16:08:19+00:00

Operations Research
Machine learning algorithms are becoming increasingly powerful, but with that power comes greater complexity and opacity. As these models become more sophisticated, they become increasingly difficult to understand—and, crucially, harder to anticipate when and how they might fail. This makes it essential to incorporate their predictions in ways that remain robust to errors. In “Single-Leg Revenue Management with Advice,” S. Balseiro, C. Kroer, and R. Kumar develop an algorithm for the classical single-leg revenue management problem that robustly incorporates predictions. They uncover a fundamental tradeoff: Placing greater trust in predictive models can yield high performance when predictions are accurate but also makes algorithms vulnerable when predictions are off. The proposed algorithm achieves the optimal tradeoff between these goals, allowing decision makers to leverage machine learning predictions while guarding against their potential inaccuracies. By doing so, this work provides a principled approach to integrating powerful yet imperfect forecasts into real-world decision making.

Maximum Load Assortment Optimization: Approximation Algorithms and Adaptivity Gaps

2026-01-14T16:08:19+00:00

Operations Research
Motivated by modern-day applications such as attended home delivery and preference-based group scheduling, where decision makers wish to steer a large number of customers toward choosing the exact same alternative, we introduce a novel class of assortment optimization problems, referred to as maximum load assortment optimization. In such settings, given a universe of substitutable products, we are facing a stream of customers, each choosing between either selecting a product out of an offered assortment or opting to leave without making a selection. Assuming that these decisions are governed by the multinomial logit choice model, we define the random load of any underlying product as the total number of customers who select it. Our objective is to offer an assortment of products to each customer so that the expected maximum load across all products is maximized. We consider both static and dynamic formulations of the maximum load assortment optimization problem. In the static setting, a single offer set is carried throughout the entire process of customer arrivals, whereas in the dynamic setting, the decision maker offers a personalized assortment to each customer, based on the entire information available at that time. As can only be expected, both formulations present a wide range of computational challenges and analytical questions. The main contribution of this paper resides in proposing efficient algorithmic approaches for computing near-optimal static and dynamic assortment policies. In particular, we develop a polynomial time approximation scheme for the static problem formulation. Additionally, we demonstrate that an elegant policy utilizing weight-ordered assortments yields a 1/2 approximation. Concurrently, we prove that such policies are sufficiently strong to provide a 1/4 approximation with respect to the dynamic formulation, establishing a constant factor bound on its adaptivity gap. Finally, we design an adaptive policy whose expected maximum load is within factor [Formula: see text] of optimal, admitting a quasi-polynomial time implementation.Funding: This work was supported by the National Science Foundation [Grant CMMI-2226900 to O. El Housni] and the Israel Science Foundation [Grant 1407/20 to D. Segev].Supplemental Material: All supplemental materials, including the code, data, and files required to reproduce the results, are available at https://doi.org/10.1287/opre.2023.0495 .

Differential Privacy via Distributionally Robust Optimization

2026-01-14T16:08:19+00:00

Operations Research
Privacy-Accuracy Trade-off from an Optimization Lens: Fix a Desired Level of Privacy, Then Maximize AccuracyIn differential privacy, the de facto standard for safeguarding individual information in data analysis, noise is added to statistics to limit the disclosure of sensitive information. Greater privacy requires more noise, creating a trade-off as the added noise reduces the accuracy of the resulting statistics. Historically, researchers have addressed this by restricting themselves to families of noise mechanisms that are sufficient for a predefined privacy level and proving their performance under specific conditions. Selvi, Liu, and Wiesemann propose a novel approach that guarantees optimal accuracy for any specified privacy level. They formulate the design of privacy mechanisms as an optimization problem that minimizes the expected loss associated with the random noise mechanism while encoding differential privacy as constraints. Through detailed analyses and by leveraging tools from distributionally robust optimization, they develop an efficient optimization algorithm and derive implementable solutions with provable guarantees to solve the problem within seconds.

Trading Prophets

2026-01-14T16:08:19+00:00

Operations Research
The prophet inequality is a cornerstone of online decision making, comparing a sequential decision maker to a prophet who knows all outcomes in advance. In “Trading Prophets,” J. Correa, A. Cristi, P. Dütting, M. Hajiaghayi, J. Olkowski, and K. Schewior initiate the study of buy-and-sell prophet inequalities. Here, an online algorithm observes a sequence of prices, one after the other, to trade an item. At each time step, the algorithm can decide to buy and pay the current price if it does not already hold the item, or it can decide to sell and collect the current price as a reward if it holds the item. The authors identify settings where a constant-factor approximation to the all-knowing prophet benchmark can be achieved. Interestingly, these conditions differ from those required for standard prophet inequalities. Specifically, they show that no constant-factor inequality exists for arbitrary independent prices. In contrast, they prove that a constant factor is achievable when independent prices arrive in a random order.

Optimal Trade Execution Under Endogenous Order Flow

2026-01-14T16:08:19+00:00

Operations Research
How should a large investor trade when their actions influence—and are influenced by—others in the market? This paper investigates optimal execution in financial markets where order flow is endogenous and governed by self-exciting dynamics. Market order arrivals are modeled using a Hawkes process, with intensity shaped by the trader’s own activity—capturing a feedback loop between execution and market response. The study considers both risk-neutral and risk-averse investors under market impact, deriving closed-form and semi–closed-form optimal strategies. In the risk-averse case, the solution skews execution toward earlier periods to mitigate inventory risk. The model is also extended to more general Hawkes kernels, enhancing practical applicability. The findings shed light on how sophisticated traders can minimize execution costs while accounting for the risk of being tracked in increasingly transparent markets. This work offers actionable insights for algorithmic execution under realistic microstructure dynamics.

On the Sparsity of Optimal Linear Decision Rules for a Class of Robust Optimization Problems with Box Uncertainty Sets

2026-01-14T16:08:19+00:00

Operations Research
One of the major reasons for the popularity of robust optimization is that these problems are often amenable to efficient approximations in operational planning problems where decisions must be made over time under uncertainty. The most common approximation is based on restricting the control policies to “linear decision rules”, that is, linear functions of the information revealed up in the past. In the paper “On the Sparsity of Optimal Linear Decision Rules for a Class of Robust Optimization Problems with Box Uncertainty Sets,” Lu and Sturt prove for a class of robust inventory management problems that there always exists an optimal linear decision rule in which the number of nonzero parameters in the linear decision rule grows linearly in the number of time periods. This is the first result to prove that optimal linear decision rules are sparse in a widely studied class of robust optimization problems with many time periods. Harnessing this sparsity guarantee, the authors develop a novel reformulation technique and active set algorithm for computing optimal linear decision rules that yield a 32× speedup over state-of-the-art linear programming solvers in numerical experiments on production–inventory problems with hundreds of time periods.

Random Graph Matching at Otter’s Threshold via Counting Chandeliers

2026-01-14T16:08:19+00:00

Operations Research
Network alignment or graph matching—figuring out how vertices across different networks correspond to each other—is a key challenge in many fields, from protecting online privacy to mapping biological data, improving computer vision, and even understanding languages. However, this problem falls into the class of notoriously difficult quadratic assignment problems, which are NP-hard to solve or approximate. Despite these challenges, researchers Mao, Wu, Xu, and Yu have made a major breakthrough. In their paper, “Random Graph Matching at Otter’s Threshold via Counting Chandeliers,” they introduce an innovative algorithm that can successfully match two random networks whenever the square of their edge correlation exceeds Otter’s constant (≈0.338). Their key innovation lies in counting chandeliers—specially designed tree-like structures—to identify corresponding vertices across the networks. The algorithm correctly matches nearly all vertices with high probability and even achieves perfect matching whenever the data allows. This is the first-ever polynomial-time algorithm capable of achieving perfect and near-perfect matching with an explicit constant correlation for both dense and sparse networks, bridging a long-standing gap between statistical limits and algorithmic performance.

Online Metric Matching: Beyond the Worst Case

2026-01-14T16:08:19+00:00

Operations Research
New Algorithms for Online Metric Matching with Stochastic Arrivals or PredictionsHow do we match riders to drivers? Online metric matching offers a clean abstraction of this problem, where arriving riders are instantaneously matched to waiting drivers and the matching cost is measured by the pickup distance. Its challenge lies in making matching decisions without knowing the locations of future riders, for which the simple greedy approach yields suboptimal solutions. In their paper “Online Metric Matching: Beyond the Worst Case,” Yang and Yu propose a novel algorithmic framework of designing algorithms for online metric matching when given access to additional information of riders’ locations in advance. They then apply this framework to derive new algorithms when the riders’ locations are independently sampled or when an untrusted prediction of riders’ locations is provided. In the former model, their algorithms achieve improved competitive ratio and regret guarantees for various settings. In the latter model, they present an algorithm whose performance smoothly depends on the prediction error while preserving the worst-case guarantee.

Dynamic Black-Litterman

2026-01-14T16:08:19+00:00

Operations Research
Incorporating Expert Views into Investment ModelsHow can investors best incorporate forward-looking expert views into dynamic portfolio strategies? “Dynamic Black-Litterman,” by Anas Abdelhakmi and Andrew Lim, offers a novel solution.The authors generalize the classic Black-Litterman model to a dynamic setting, allowing for continuous trading and expert views of events over varying time horizons that arrive over time. They derive the explicit dynamics of asset prices after incorporating these views, uncovering a surprising and elegant connection to multidimensional Brownian bridges.Remarkably, the paper provides a closed-form solution for the optimal dynamic portfolio policy, a significant finding for a model of this complexity. This includes a specific hedging component to protect against changes in expert views. Numerical experiments demonstrate that this dynamic approach consistently outperforms strategies that repeatedly apply the single-period model, leading to higher returns and lower portfolio turnover.

Multilocation, Dynamic Staff Planning for a Healthcare System: Methodology and Application

2026-01-14T16:08:19+00:00

Operations Research
Multilocation, Dynamic Staff Planning for a Healthcare System: Methodology and ApplicationThis study develops and implements a robust optimization model for dynamically assigning anesthesiologists across multiple hospitals in a large health system. The model addresses uncertainty in surgical demand by incorporating a three-stage decision process: presurgical location assignments, midhorizon on-call deployment, and day-of realization of overtime or idleness. The authors formulate the problem as a multistage robust mixed-integer program and solve it efficiently using a novel nested column and constraint generation algorithm. The approach accounts for location-specific constraints and fairness in on-call duties using historical data to estimate demand uncertainty with a calibrated trade-off between optimality and robustness. Implemented at the University of Pittsburgh Medical Center, the model reduced annual staffing costs by 12% or roughly $800,000 compared to existing practice. The framework is generalizable to other staffing problems in healthcare and provides operational insights on the value of forecast accuracy, location flexibility, and equitable workload distribution.

Adaptive Learning in Uncertain and Sequential Competition

2026-01-14T16:08:19+00:00

Operations Research
In competitive markets, companies often lack access to their rivals’ sales, costs, and strategies. Can they still learn to make optimal decisions? In a new study, Li and Mehrotra show that the answer is yes. Their research demonstrates that even without competitor data, firms can adaptively learn to make near-optimal choices using only their own operational information. More strikingly, when all players follow such self-driven learning, the entire market converges to a Nash equilibrium—the stable state predicted by economic theory—without explicit coordination. The study establishes theoretical guarantees for both convergence rates and regret performance and illustrates the framework in inventory management and dynamic pricing settings. These findings provide a foundation for data-driven decision making in competitive and uncertain environments and offer insights into how markets naturally self-organize.

Robust Actionable Prescriptive Analytics

2026-01-14T16:08:19+00:00

Operations Research
In "Robust Actionable Prescriptive Analytics," Chen et al. present a significant advancement in prescriptive analytics. The authors propose a novel robust prescriptive analytics framework that bridges data-driven decision making and actionable policy optimization. Unlike traditional approaches that follow a “predict, then optimize” methodology, this framework directly maps side information to optimized decisions, ensuring both interpretability and implementability. Leveraging a robust satisficing approach, the model effectively mitigates overfitting to empirical data while maintaining computational tractability. The authors also introduce tree-based static and affine policies for enhanced interpretability, and they demonstrate the framework’s practical value through a portfolio optimization case study. This innovative approach provides a powerful tool for decision makers seeking robust, data-driven policies across various operational contexts.

Technical Note—What’s in a Constraint? On the Ambiguity of Standard Delay Targets

2026-01-14T16:08:19+00:00

Operations Research
Ambiguities in Average Speed of Answer TargetsStaffing problems are often formulated as satisfization problems, in which the cost of servers is minimized subject to quality of service constraints. These constraints are intended to capture customers’ disutility from waiting or, at least, its structure. In “What’s in a constraint? On the ambiguity of standard delay targets,” Soh and Gurvich show that such targets—especially the popular average speed of answer—are ambiguous: they give rise to multiple optimal solutions (prioritization policies), each consistent with different assumptions about how customers value their time. By choosing, among all optimal solutions, the one that minimizes a weighted index of diversion (a generalization of variance for the multiclass queue), a service provider can ensure that its ASA-based staffing and prioritization decisions align with a convex model of customer delay disutility. Nonambiguity can also be enforced by restricting attention to fixed queue ratio priority policies.

Event-Triggered Bayesian Control Chart

2026-01-14T16:08:19+00:00

Operations Research
Control charts are practical tools for fault detection and recovery. However, traditional control charts rely on random samples collected from a production process at fixed time intervals, causing late detection if sampling intervals are too long or excessive sampling if the intervals are too short. In “Event-Triggered Bayesian Control Chart,” Abbou and Makis develop a novel control chart leveraging real-time data from smart sensors to jointly decide when to collect samples and when to stop the production process, leading to quick fault detection and recovery using few samples. Applying optimal stopping theory and dynamic programming analysis, the authors establish the average-cost optimality of their control chart and propose an efficient procedure for computing the optimal sampling and stopping thresholds. Through an empirical study, the control chart is shown to achieve substantial cost savings compared to benchmarks. Furthermore, thanks to its event-triggering mechanism, the proposed control chart requires little data communication from sensors, which is crucial from an energy-efficiency perspective.

Building Formulations for Piecewise Linear Relaxations of Nonlinear Functions

2026-01-14T16:08:19+00:00

Operations Research
We study mixed-integer programming formulations for the piecewise linear lower and upper bounds (in other words, piecewise linear relaxations) of nonlinear functions that can be modeled by a new class of combinatorial disjunctive constraints (CDCs), generalized nD-ordered CDCs. We first introduce a general formulation technique to model piecewise linear lower and upper bounds of univariate nonlinear functions concurrently so that it uses fewer binary variables than modeling bounds separately. Next, we propose logarithmically sized ideal nonextended formulations to model the piecewise linear relaxations of univariate and higher-dimensional nonlinear functions under the CDC and independent branching frameworks. We also perform computational experiments for the approaches modeling the piecewise linear relaxations of nonlinear functions and show significant speed-ups of our proposed formulations. Furthermore, we demonstrate that piecewise linear relaxations can provide strong dual bounds of the original problems with less computational time by an order of magnitude.Funding: This work was supported by the Office of Naval Research [Grant N000142412648] for Rice University.Supplemental Material: The online appendices are available at https://doi.org/10.1287/opre.2023.0187 .

Process Flexibility: A Distribution-Free Approach to Long Chain Resilience

2026-01-14T16:08:19+00:00

Operations Research
A New Perspective on Resilience in Flexible Supply SystemsSupply disruptions and demand uncertainty are persistent threats in today’s global operations landscape. In “Process Flexibility: A Distribution-Free Approach to Long Chain Resilience,” Chen, Chou, and Sun revisit the long-standing concept of long chain flexibility and demonstrate its resilience in the face of supply-side risks. By deriving a closed-form bound on expected sales relative to full flexibility, the study offers a robust, distribution-free guarantee that highlights the long chain’s ability to hedge against disruptions. The authors further generalize their findings using a novel moment decomposition approach, extending applicability to a broader range of service metrics and capacity-demand scenarios via semidefinite programming. Their results not only reaffirm the demand-pooling power of long chains but also position them as a highly resilient configuration in disrupted environments, offering practical insights for capacity planning in uncertain supply chains.

Rate-Optimal Online Learning for Dynamic Assortment Selection with Positioning

2026-01-14T16:08:19+00:00

Operations Research
This study addresses a key challenge in online retail: product positioning. The authors propose a novel online learning framework called dynamic assortment selection with positioning (DAP). Unlike traditional models that focus solely on item selection, DAP also learns optimal product placement to maximize revenue. The researchers model customer choices using a multinomial logit framework, where item appeal depends on both intrinsic preference and display position. They demonstrate that ignoring position effects leads to suboptimal performance and introduce a new algorithm, TLR-UCB, which effectively incorporates adaptive position-dependent feedback through a geometric linear bandit structure and truncated linear regression techniques. Theoretical analysis confirms that TLR-UCB achieves optimal learning efficiency. To handle unknown position effects, they further develop EI-TLR, a two-stage policy that jointly estimates customer preferences and positioning impacts before applying a generalized TLR-UCB procedure. Extensive simulations show that both TLR-UCB and EI-TLR significantly outperform existing benchmarks, offering powerful tools for dynamic, data-driven assortment and layout optimization in online marketplaces.

A Data-Driven Approach to Improve Artisans’ Productivity in Distributed Supply Chains

2026-01-14T16:08:19+00:00

Operations Research
Smarter Supervision Lifts Rural Weavers’ ProductivityFrequent, predictable supervisor visits can be a powerful lever for boosting artisan productivity in distributed supply chains, according to a study conducted with Jaipur Rugs in India. Analyzing loom-level data, the authors show that reducing the average gap between visits by just one day raises weaving rates by 8.5%—with more substantial gains on complex rugs and when visits follow consistent schedules. Building on these insights, they develop a routing and scheduling framework that targets those looms most in need of support. In a 25-week field implementation covering about 6,000 visits across 200 looms, sites assigned to optimized routes saw a 16.7% increase in weaving speed relative to controls, highlighting a practical, low-cost pathway to higher earnings for rural women weavers. The research suggests that data-driven supervision in other supply chains with a similar structure (e.g., smallholder agriculture) could boost productivity and earnings, offering an operational lever for poverty alleviation at scale.

Matching Queues with Abandonments in Quantum Switches: Stability and Throughput Analysis

2026-01-14T16:08:19+00:00

Operations Research
Researchers have developed a novel model inspired by quantum switches to address the complexities of matching requests for entangled qubits in a discrete-time system. The study examines two types of arrivals: requests for entangled qubits between nodes and qubits supplied by nodes, which are subject to decoherence over time. Unlike classical queueing models, this system features server-less multiway matching and correlated abandonments, posing unique analytical challenges. By applying a max-weight policy, the researchers characterized the system’s stability using a two-time-scale fluid limit to account for qubit abandonments. They demonstrated that the max-weight policy is throughput optimal, outperforming nonidling policies under certain conditions. Intriguingly, the study revealed counterintuitive behavior: The longest request queue may grow temporarily, even in a stable system. These findings offer new insights into managing quantum-inspired systems with practical constraints, opening avenues for further research into quantum network optimization.

Spectral Ranking Inferences Based on General Multiway Comparisons

2026-01-14T16:08:19+00:00

Operations Research
Ranking Inferences for General Multiway ComparisonsRanking inference underpins critical decision-making across diverse domains including e.g. university ranking, journal ranking, academic paper review, voting, online recommendation and tournament competition. Beyond generating point estimates of ranks, these applications also demand confidence intervals for ranks through robust uncertainty quantification, in order to ensure reliable and informed decisions. However, existing approaches predominantly rely on classical models such as Bradley-Terry-Luce and Plackett-Luce, which assume homogeneous comparison structures and prove inadequate for complex real-world scenarios. This paper presents a unified spectral ranking methodology for heterogeneous multiway comparisons, which simultaneously achieves statistical efficiency under minimal structural assumptions and computational scalability. The authors establish comprehensive ranking inference tools, grounded in the asymptotic normality theory and bootstrapping techniques, facilitating top-K selection, rank confidence interval construction, and hypothesis testing for cross-population or cross-period ranking consistency.

Minimax Optimal Estimation of Stability Under Distribution Shift

2026-01-14T16:08:19+00:00

Operations Research
How to Efficiently Estimate Stability Measures: A Minimax Optimal ApproachIn “Minimax Optimal Estimation of Stability Under Distribution Shift,” Hongseok Namkoong, Yuanzhe Ma, and Peter W. Glynn address the challenge of benchmarking the performance of decision policies and prediction models under distribution shift. Conventional risk measures and distributionally robust losses typically require specifying the magnitude of possible distribution shift—a quantity that is difficult to determine in practice. Instead, the authors consider a stability measure defined in terms of the acceptable level of performance degradation, which is more intuitive. To efficiently estimate this measure, they consider an estimator based on the dual formulation of the stability measure and show that this estimator is minimax optimal. Their results quantify the convergence rate of the estimator, which exhibits a fundamental phase shift behavior. In addition, they empirically observe that the stability measure reliably captures system performance under distribution shift in applications including queueing systems and healthcare prediction tasks.

Binary Diversification Characterizes Exact Capacities

2026-01-14T16:08:19+00:00

Operations Research
Axiomatic Insights into Ambiguity Aversion: Binary Diversification and Exact CapacitiesIn decision sciences, ambiguity refers to uncertainty about event probabilities, and aversion to it is a well-studied phenomenon. In “Binary Diversification Characterizes Exact Capacities,” Hartmann and Kauffeldt consider the Choquet expected utility model, a framework for modeling ambiguity. They provide the first axiomatic characterization of exact capacities, solving an old problem. Their characterizing axiom, binary diversification, captures a specific level of ambiguity aversion—a preference for diversifications that lead to (at most) binary outcomes. They also propose an intuitive hierarchy of ambiguity aversion and illustrate that this hierarchy reflects increasingly strong levels of ambiguity aversion within the more general invariant biseparable model, whereas the hierarchy has limited bite in the Choquet model. They conclude by illustrating the implications for multiobjective shortest-path problems, demonstrating how the results can be applied.

Is Your Price Personalized? Alleviating Customer Concerns with Inventory Availability Information

2026-01-14T16:08:19+00:00

Operations Research
Can Customers Spot Price Discrimination?Firms adjust prices to match supply with demand and increasingly use customer data to personalize pricing. Consequently, customers have limited visibility into price drivers and may worry about price discrimination based on their shopping behavior. For instance, a customer with high willingness to pay may receive a high price either because the product is popular or because the price has been personalized. In “Is Your Price Personalized? Alleviating Customer Concerns with Inventory Availability Information,” A. Aflaki and Q. Zhang studied whether observed prices are sufficiently informative to signal personalization in a dynamic setting. They found that price alone cannot resolve this uncertainty, which may lead to actions that harm both firms and customers. However, a simple binary signal disclosing whether inventory is low or abundant can, under certain conditions, ease customer concerns and benefit both the firm and its customers.

In This Apportionment Lottery, the House Always Wins

2026-01-14T16:08:19+00:00

Operations Research
Randomized Apportionment: A Fairer Distribution of SeatsThe question of how to apportion the seats of the U.S. House of Representatives to states has fueled century-long political debates and sparked mathematical theory. Traditional deterministic methods, such as the Hamilton method or the currently used Huntington–Hill method, may result in paradoxes or substantially deviate from proportionality. In their paper “In This Apportionment Lottery, the House Always Wins,” Gölz, Peters, and Procaccia propose a randomized approach that ensures each state receives its exact proportional share of seats in expectation and its proportional share, up to rounding, ex post. By incorporating randomization, the authors argue, the system can better adhere to the principle of proportional representation, minimizing the impact of small counting errors and ensuring fairness over time. In addition, their approach achieves house monotonicity, a property that prevents counterintuitive outcomes when the total number of seats changes. This is achieved through a novel cumulative rounding technique, a generalization of dependent rounding on bipartite graphs with potential applications beyond apportionment, including EU commission nominations and resource allocation.

Package Bids in Combinatorial Electricity Auctions: Selection, Welfare Losses, and Alternatives

2026-01-14T16:08:19+00:00

Operations Research
Day-ahead electricity auctions allow market participants to trade power for delivery the following day. In Europe, these auctions are designed as combinatorial auctions, enabling agents to submit package bids (“block bids”) that span multiple time periods rather than bidding separately for each hour. However, power exchanges impose limits on the number of package bids an agent can submit, creating a complex decision problem: Which packages should an agent bid on to best represent their preferences? In “Package Bids in Combinatorial Electricity Auctions: Selection, Welfare Losses, and Alternatives,” Hübner and Hug study this selection problem and propose decision-support algorithms that optimize bid choice under uncertainty. They provide theoretical bounds on welfare loss due to bid limits and validate their methods with simulations involving generators, storage systems, and flexible demand. Their findings offer actionable insights for both auctioneers and bidders.

Estimating Position and Social Influence Effects in Online Search

2026-01-14T16:08:19+00:00

Marketing Science
This paper estimates position and social influence effects in online search using a structural model and field experiment data.

Applying Large Language Models to Sponsored Search Advertising

2026-01-14T16:08:19+00:00

Marketing Science
This paper presents a human-in-the-loop LLM framework that boosts search engine ad performance and explores cost trade-offs and boundary conditions.

When Less Is More: Content Strategies for Subscription Video on Demand

2026-01-14T16:08:19+00:00

Marketing Science
A field experiment shows that gradual content release boosts engagement and retention more than binge release on a streaming platform.

Vertical Competition on a Common Platform

2026-01-14T16:08:19+00:00

Marketing Science
We investigate how a platform decides on the provision and pricing of its infrastructure for vertically differentiated vendors when facing vendors’ strategic reactions.

Investigating the Impact of Advertising on Smoking Cessation: The Role of Direct-to-Consumer Prescription Drug Advertising

2026-01-14T16:08:19+00:00

Marketing Science
This paper measures the effects of direct-to-consumer advertising for smoking cessation prescription drugs and examines how access barriers moderate these effects.

Is Fair Advertising Good for Platforms?

2026-01-14T16:08:19+00:00

Marketing Science
The paper analyzes various fairness policies for ensuring fairness in ad-auctions.

Gender-Based Pricing in Consumer Packaged Goods: A Pink Tax?

2026-01-14T16:08:19+00:00

Marketing Science
This paper studies the extent to which firms segment, differentiate, and price discriminate on the basis of gender in the personal care category.

Bad Apples on Rotten Tomatoes: Critics, Crowds, and Gender Bias in Product Ratings

2026-01-14T16:08:19+00:00

Marketing Science
This paper shows that movie crowd reviewers provide evaluations that are biased against movies with a stronger female presence relative to professional critics.

Symbolic vs. Substantive Support: The Impact of Black Lives Matter on Black-Owned Businesses

2026-01-14T16:08:19+00:00

Marketing Science
This study examines the impact of Black Lives Matter (BLM) on Black-owned businesses following the murder of George Floyd.

Job Security, Gender, and Sales Performance: Evidence from a Retail Sales Context

2026-01-14T16:08:19+00:00

Marketing Science
This paper investigates the impact of job stability on the performance of workers, specifically women.

Can Rising Eco-sensitivity Hurt Sustainability? Eco-impact of Durable Goods Innovations

2026-01-14T16:08:19+00:00

Marketing Science
We analyze the ecoimpact of different types of durable goods innovations under rising consumer ecosensitivity based on a dynamic two-period framework.

Shrinkflation and Consumer Demand

2026-01-14T16:08:19+00:00

Marketing Science
Using decade-long U.S. grocery scanner data, we quantify shrinkflation’s prevalence and show consumers scarcely adjust demand despite higher per-ounce prices.

Regional Poverty Alleviation Partnership and E-Commerce Trade

2026-01-14T16:08:19+00:00

Marketing Science
This paper examines how the East-West Poverty Alleviation Partnership, which pairs rich cities in East China with economically disadvantaged cities in West China, affects e-commerce trade.

The Evolution of Discrimination in Online Markets: How the Rise in Anti-Asian Bias Affected Airbnb During the Pandemic

2026-01-14T16:08:19+00:00

Marketing Science
Anti-Asian bias surged on Airbnb during COVID-19. Hosts with Asian names experienced a 20% decline in guests, resulting in $180–$330 monthly losses.

Interacting with Man or Machine: When Do Humans Reason Better?

2026-01-14T16:08:19+00:00

Management Science
The resolution of complex problems is widely seen as the next challenge for hybrid human–artificial intelligence (AI) teams. This paper uses experiments to assess whether there is a difference in the quality of human reasoning depending on whether the humans interact with humans or algorithms. For this purpose, we design an interactive reasoning task and compare the performance of humans when paired with other humans and AI. Varying the difficulty of the task (i.e., steps of counterfactual reasoning required), we find that, for simple tasks, subjects perform much better if they play with other humans, whereas the opposite is true for difficult problems. Additional experiments in which subjects play with human experts show that the differences are driven by the knowledge that AI reasons correctly rather than that it is nonhuman.This paper was accepted by Elena Katok, Special Issue on the Human-Algorithm Connection.Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.03315 .

Humans’ Use of AI Assistance: The Effect of Loss Aversion on Willingness to Delegate Decisions

2026-01-14T16:08:19+00:00

Management Science
As artificial intelligence (AI) tools have become pervasive in business applications, so too have interactions between AI and humans in business processes and decision-making. A growing area of research has focused on human decision and task delegation to AI assistants. Simultaneously, extensive research on algorithm aversion—humans’ resistance to algorithm-based decision tools—has demonstrated potential barriers and issues with AI applications in business. In this paper, we test a simple strategy for mitigating algorithm aversion in the context of AI task delegation. We show that simply changing the framing of decision tasks can allay algorithm aversion. Through multiple studies, we found that participants exhibited a strong preference for human assistance over AI assistance when they were rewarded for task performance (i.e., money was gained for good performance), even when the AI had been shown to outperform the human assistant on the task. Alternatively, when we reframed the task such that the participant experienced losses for poor performance (i.e., money was taken from their endowment for poor performance), the bias for preferring human assistance was removed. Under loss framing, participants delegated the decision task to human and AI assistants at similar rates. We demonstrate this finding across tasks at differing levels of complexity and at different incentive sizes. We also provide evidence that loss framing increases situational awareness, which drives the observed effects. Our results offer useful insights on reducing algorithm aversion that extend the literature and provide actionable suggestions for practitioners and managers.This paper was accepted by Dongjun Wu, Special Issue on the Human-Algorithm Connection.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05585 .

Behavioral Externalities of Process Automation

2026-01-14T16:08:19+00:00

Management Science
We study the behavioral effects of process automation on human workers interacting with automated tasks. We introduce a stylized normative model with two workers who complete their tasks sequentially, working toward a joint project to obtain a fixed payment plus a variable bonus that depends on how early the project is completed. The normative model prescribes that, if workers are fully rational, they will complete their tasks as soon as possible if the early completion bonus is high enough. However, following the literature, we hypothesize that workers will suboptimally delay project completion. Following the insights from a behavioral model, we further predict that automation will alleviate this problem by reducing strategic uncertainty, resulting in an indirect behavioral benefit of a higher worker productivity, in addition to the direct benefit of a higher project completion rate and a shorter project duration. To test these predictions, we conduct an experiment replicating the theoretical model, varying (i) whether a worker collaborates with a coworker or a robot, and (ii) in the case of collaborating with a robot, whether the upstream or downstream task is the one automated. First, we find that workers largely deviate from the optimal policy, as they take longer than what the normative theory prescribes to complete their tasks or do not complete the project. Second, we show that process automation increases the project completion rate and reduces the project completion time, confirming the benefits of process automation. Interestingly, workers who collaborate with robots take longer to complete their tasks, contradicting our initial hypothesis that process automation has a positive effect on the productivity of human workers. In addition, we find that upstream automation is more beneficial than downstream automation. We also show that social preferences are an important driver of these results because prosocial subjects tend to be more productive when collaborating with a human coworker than with a robot. Finally, we show that our findings remain robust in a continuous processing setting.This paper was accepted by Felipe Caro, Special Issue on the Human-Algorithm Connection.Funding: The authors gratefully acknowledge financial support from Zicklin School of Business, Baruch College, the City University of New York, and the University of Texas at Dallas. Support for this project was provided by a PSC-CUNY Award, jointly funded by the Professional Staff Congress and the City University of New York.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05408 .

Reciprocal Human-Machine Learning: A Theory and an Instantiation for the Case of Message Classification

2026-01-14T16:08:19+00:00

Management Science
There is growing agreement among researchers and developers that in certain machine-learning (ML) tasks, it may be advantageous to keep a “human in the loop” rather than rely on fully autonomous systems. Continual human involvement can mitigate machine bias and performance deterioration while enabling humans to continue learning from insights derived by ML. Yet a microlevel theory that effectively facilitates joint and continual learning in both humans and machines is still lacking. To address this need, we adopt a design science approach and build on theories of human reciprocal learning to develop an abstract configuration for reciprocal human-ML (RHML) in the context of text message classification. This configuration supports learning cycles between humans and machines who repeatedly exchange feedback regarding a classification task and adjust their knowledge representations accordingly. Our configuration is instantiated in Fusion, a novel technology artifact. Fusion is developed iteratively in two case studies of cybersecurity forums (drug trafficking and hacker attacks), in which domain experts and ML models jointly learn to classify textual messages. In the final stage, we conducted two experiments of the RHML configuration to gauge both human and machine learning processes over eight learning cycles. Generalizing our insights, we provide formal design principles for the development of systems to support RHML.This paper was accepted by D. J. Wu, Special Issue on the Human-Algorithm Connection.Funding: This work was supported by the Israel’s Ministry of Defence [Grant R4441197567] and the Israel’s Ministry of Science and Technology [Grant 207076].Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.03518 .

Incentives, Framing, and Reliance on Algorithmic Advice: An Experimental Study

2026-01-14T16:08:19+00:00

Management Science
Managerial decision makers are increasingly supported by advanced data analytics and other artificial intelligence (AI)-based technologies, but they are often found to be hesitant to follow the algorithmic advice. We examine how compensation contract design and framing of an AI algorithm influence decision makers’ reliance on algorithmic advice and performance in a price estimation task. Based on a large sample of almost 1,500 participants, we find that compared with a fixed compensation, both compensation contracts based on individual performance and tournament contracts lead to an increase in effort duration and to more reliance on algorithmic advice. We further find that using an AI algorithm that is framed as also incorporating human expertise has positive effects on advice utilization, especially for decision makers with fixed pay contracts. By showing how widely used control practices, such as incentives and task framing, influence the interaction of human decision makers with AI algorithms, our findings have direct implications for managerial practice.This paper was accepted by David Simchi-Levi, Special Issue on the Human-Algorithm Connection.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02777 .

Improving Human Sequential Decision Making with Reinforcement Learning

2026-01-14T16:08:19+00:00

Management Science
Workers spend a significant amount of time learning how to make good decisions. Evaluating the efficacy of a given decision, however, can be complicated—for example, decision outcomes are often long-term and relate to the original decision in complex ways. Surprisingly, even though learning good decision-making strategies is difficult, the strategies can often be expressed in simple and concise forms. Focusing on sequential decision making, we design a novel machine learning algorithm that is capable of extracting “best practices” from trace data and conveying its insights to humans in the form of interpretable “tips.” Our algorithm selects the tip that best bridges the gap between the actions taken by human workers and those taken by the optimal policy in a way that accounts for which actions are consequential for achieving higher performance. We evaluate our approach through a series of randomized controlled experiments where participants manage a virtual kitchen. Our experiments show that the tips generated by our algorithm can significantly improve human performance relative to intuitive baselines. In addition, we discuss a number of empirical insights that can help inform the design of algorithms intended for human-AI interfaces. For instance, we find evidence that participants do not simply blindly follow our tips; instead, they combine them with their own experience to discover additional strategies for improving performance.This paper was accepted by Elena Katok, Special Issue on the Human-Algorithm Connection.Funding: This work was supported by the Mack Institute for Innovation Management, the Berkeley Artificial Intelligence Research Open Research Commons, and The Wharton Behavioral Lab.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02455 .

Till Tech Do Us Part: Betrayal Aversion and Its Role in Algorithm Use

2026-01-14T16:08:19+00:00

Management Science
Failing to follow expert advice can have real and dangerous consequences. While any number of factors may lead a decision maker to refuse expert advice, the proliferation of algorithmic experts has further complicated the issue. One potential mechanism that restricts the acceptance of expert advice is betrayal aversion, or the strong dislike for the violation of trust norms. This study explores whether the introduction of expert algorithms in place of human experts can attenuate betrayal aversion and lead to higher overall rates of seeking expert advice. In other words, we ask: are decision makers averse to algorithmic betrayal? The answer to this question is uncertain ex ante. We answer this question through an experimental financial market where there is an identical risk of betrayal from either a human or algorithmic financial advisor. We find that the willingness to delegate to human experts is significantly reduced by betrayal aversion, while no betrayal aversion is exhibited toward algorithmic experts. The impact of betrayal aversion toward financial advisors is considerable: the resulting unwillingness to take the advice of the human expert leads to a 20% decrease in subsequent earnings, while no loss in earnings is observed in the algorithmic expert condition. This study has significant implications for firms, policymakers, and consumers, specifically in the financial services industry.This paper was accepted by D. J. Wu, Special Issue on the Human-Algorithm Connection.Funding: This work was supported by National Science Foundation [Grant 1541105].Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.03510 .

When Emotion AI Meets Strategic Users

2026-01-14T16:08:19+00:00

Management Science
When organizations adopt artificial intelligence (AI) to recognize individuals’ negative emotions and accordingly allocate limited resources, strategic users are incentivized to game the system by misrepresenting their emotions. The value of AI in automating such emotion-driven allocation may be undermined by gaming behavior, algorithmic noise in emotion detection, and the spillover effect of negative emotions. We develop a game-theoretical model to understand emotion AI adoption, particularly in customer care, and analyze the design of the associated allocation policies. We find that adopting emotion AI is valuable if the spillover effect of negative emotions is negligible compared with resource misallocation loss, regardless of algorithmic noise and gaming behavior. We also quantify the welfare impacts of emotion AI on the users, organization, and society. Notably, a stronger AI is not always socially desirable and regulation on emotion-driven allocation is needed. Finally, we characterize conditions under which leveraging the AI system is preferred to hiring human employees in emotion-driven allocation. We also explore the alternative application of using emotion AI to monitor strategic employees and compare it with hiring a human manager for monitoring. Intriguingly, algorithmic noise may increase the profit of AI monitoring. Our work provides implications for designing, adopting, and regulating emotion AI.This paper was accepted by D. J. Wu, Special Issue on the Human-Algorithm Connection.Funding: The work of L. Jia was supported by the National Natural Science Foundation of China [Grant 72172013].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02860 .

Speaking in Private: Privacy Expectations Depend on Communication Modality

2026-01-14T16:08:19+00:00

Management Science
Consumers disclose personal information when they interact with connected technologies. The advent of voice technology has enabled consumers to interact with connected technologies not only through typing but also through speaking. The present research investigates whether consumers expect different levels of privacy for information they disclose via different communication modalities. The results of three studies suggest that consumers have more restrictive privacy expectations for information disclosed via speech as compared with text. The studies probe the viability of several mechanisms that may drive this effect and test practically relevant moderators. The results suggest that the effect is driven, at least in part, by increased feelings of ownership over content disclosed via speech as compared with text. Of relevance to multiple stakeholders, the article discusses implications for privacy regulation, privacy-preserving application design, targeted advertising, and the “privacy paradox.”This paper was accepted by Catherine Tucker, Special Issue on the Human-Algorithm Connection.Funding: This work was supported by Marketing Science Institute [4001309], NYU Stern Center for Global Economy and Business.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03954 .

Human-Centered Artificial Intelligence: A Field Experiment

2026-01-14T16:08:19+00:00

Management Science
Humans and artificial intelligence (AI) algorithms increasingly interact on unstructured managerial tasks. We propose that tailoring this human-AI interaction to align with individuals’ cognitive preferences is essential for enhancing performance. This hypothesis is examined through a field experiment in a multinational pharmaceutical firm. In the experiment, we manipulated four contextual parameters of human-AI interaction—work procedures, decision-making authority, training, and incentives—to align with sales experts’ cognitive styles, categorized as either adaptors or innovators. Our results show that tailored interaction significantly improves sales performance, whereas untailored interaction results in negative treatment effects compared with both the tailored and control conditions. Qualitative evidence suggests that this negative outcome arises from role conflicts and ambiguities in untailored interaction. Exploring the mechanisms underlying these outcomes further, a mediation analysis of AI login data reveals that human-AI interaction tailoring leads sales experts to adjust their AI utilization, which contributes to the observed performance outcomes. These findings support a human-centered approach to AI that prioritizes individuals’ information-processing needs and tailors their interaction with AI accordingly.This paper was accepted by Catherine Tucker, Special Issue on the Human-Algorithm Connection.Funding: This work was supported by Erling Persson Family Foundation; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung [Grants 100013M 204670, 181364, 185164]; Jan Wallanders och Tom Hedelius Stiftelse samt Tore Browaldhs Stiftelse [Grant W20-0036]; Marianne and Marcus Wallenberg Foundation [Grants 2021.0074, 2021.0133].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03849 .

Using AI and Behavioral Finance to Cope with Limited Attention and Reduce Overdraft Fees

2026-01-14T16:08:19+00:00

Management Science
We test how effective a human–algorithm interaction is at stopping users from overdrawing their bank accounts. We use a randomized field experiment and draw our sample from users of a large personal financial management platform operating in the United States and Canada. We find that sending as-needed reminders is effective in and of itself, and the impact is intensified by the human response to the structure of the message. More simple messages are more effective, and the framing of the simplified message makes a difference. Users with medium to high annual incomes and users with fair to good credit scores are most likely to respond positively. We find that the investigated artificial intelligence solution reduces information-gathering costs and has a positive effect but is not sufficient in all cases. Those with challenging financial situations may find it harder to act upon the warning. For our analysis, we employ parametric identifications and time-to-event semiparametric analysis. Our work contributes to the literature on financial technology as advisors, human–computer interaction, limited attention, behavioral finance, and experimental finance.This paper was accepted by Jean-Edouard Colliard, Special Issue on the Human-Algorithm Connection.Funding: O. Sade acknowledges financial support for this research from the Krueger Center and the Albertson-Waltuch Chair in Business Administration.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00304 .

Managerial Insight and “Optimal” Algorithms

2026-01-14T16:08:19+00:00

Management Science
Work is increasingly being completed by humans and algorithms in collaboration. A relative strength of humans in this partnership is their insight: private information that is relevant to the task but not available to computerized systems. I introduce a flexible model of managerial insight that accepts any distribution of demand, an advantage over alternative models, and apply it to the newsvendor setting. The optimal policy in this setting is theoretically straightforward but difficult for managers to implement directly. I propose a novel method called FIND that leverages historical forecasts to convert a point estimate of demand into a conditional probability distribution. In eight experiments, FIND outperforms all other ordering regimes considered over a broad range of conditions. To model subtle, unstructured demand signals, the last four experiments convey managerial insight nonquantitatively using images, colors, and tones. FIND performs equally well with these perceptual signals as it does with more traditional numerical signals.This paper was accepted by Felipe Caro, Special Issue on the Human-Algorithm Connection.Funding: This research was supported by the National Science Foundation (Award No. 1729837) and the Research Grant Program of the Darla Moore School of Business, University of South Carolina.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03919 .

My Advisor, Her AI, and Me: Evidence from a Field Experiment on Human–AI Collaboration and Investment Decisions

2026-01-14T16:08:19+00:00

Management Science
Amid ongoing policy and managerial debates on keeping humans in the loop of artificial intelligence (AI) decision-making processes, we investigate whether human involvement in AI-based service production benefits downstream consumers. Partnering with a large savings bank in Europe, we produced pure AI and human–AI collaborative investment advice, which we passed to the bank customers and investigated the degree of their advice taking in a field experiment. On the production side, contrary to concerns that humans might inefficiently override AI output, our findings show that having a human banker in the loop of AI-based financial advisory by giving her the final say over the advice provided does not compromise the quality of the advice. More importantly, on the consumption side, we find that the bank customers are more likely to align their final investment decisions with advice from the human–AI collaboration, compared with pure AI, especially when facing more risky investments. In our setting, this increased reliance on human–AI collaborative advice leads to higher material welfare for consumers. Additional analyses from the field experiment along with an online controlled experiment indicate that the persuasive efficacy of human–AI collaborative advice cannot be attributed to consumers’ belief in increased advice quality resulting from complementarities between human and AI capabilities. Instead, the consumption-side benefits of human involvement in the AI-based service largely stem from human involvement serving as a peripheral cue that enhances the affective appeal of the advice. Our findings indicate that regulations and guidelines should adopt a consumer-centric approach by fostering environments where human capabilities and AI systems can synergize effectively to benefit consumers while safeguarding consumer welfare. These nuanced insights are crucial for managers who face decisions about offering pure AI versus human–AI collaborative services and also for regulators advocating for having humans in the loop.This paper was accepted by D. J. Wu, Special Issue on the Human-Algorithm Connection.Funding: This work was supported by a Hi!PARIS fellowship.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03918 .

The Best Decisions Are Not the Best Advice: Making Adherence-Aware Recommendations

2026-01-14T16:08:19+00:00

Management Science
Many high-stake decisions follow an expert-in-loop structure in that a human operator receives recommendations from an algorithm but is the ultimate decision maker. Hence, the algorithm’s recommendation may differ from the actual decision implemented in practice. However, most algorithmic recommendations are obtained by solving an optimization problem that assumes recommendations will be perfectly implemented. We propose an adherence-aware optimization framework to capture the dichotomy between the recommended and the implemented policy and analyze the impact of partial adherence on the optimal recommendation. Our framework provides useful tools to analyze the structure and to compute optimal recommendation policies that are naturally immune against such human deviations and are guaranteed to improve upon the baseline policy.This paper was accepted by Nicolas Stier-Moses, Special Issue on the Human-Algorithm Connection.Funding: J. Grand-Clément was supported by the Agence Nationale de la Recherche [Grant 11-LABX-0047] and Hi! Paris.Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.01851 .

Strategic Inattention in Product Search

2026-01-14T16:08:19+00:00

Management Science
Rapid technological developments in online markets fundamentally change the relationship between consumers and sellers. Online platforms can easily gather data about consumers’ search behavior, allowing for price discrimination. Therefore, product search becomes a strategic choice. Consumers face a tradeoff: Search intensely and receive a better fit at a potentially higher price or restrict search behavior, be strategically inattentive, and receive a worse fit but maybe a better deal. We study the resulting strategic buyer-seller interaction theoretically and experimentally. Our experimental results shed a critical light on the added value for consumers through the rise of online platforms and on the effectiveness of current regulations.This paper was accepted by Elena Katok, Special Issue on the Human-Algorithm Connection.Funding: This work was supported by the ZEW–Leibniz-Zentrum für Europäische Wirtschaftsforschung, and the Max-Planck-Gesellschaft.Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.03413 .

Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information

2026-01-14T16:08:19+00:00

Management Science
Motivated by the human-machine interaction such as recommending videos for improving customer engagement, we study human-guided human-machine interaction for decision making with private information. We model this interaction as a two-player turn-based game, where one player (Bob, a human) guides the other player (Alice, a machine) toward a common goal. Specifically, we focus on offline reinforcement learning (RL) in this game, where the goal is to find a policy pair for Alice and Bob that maximizes their expected total rewards based on an offline data set collected a priori. The offline setting presents two challenges: (i) We cannot collect Bob’s private information, leading to a confounding bias when using standard RL methods, and (ii) there is a distributional mismatch between the behavior policy used to collect data and the desired optimal policy we aim to learn. To tackle the confounding bias, we treat Bob’s previous action as an instrumental variable for Alice’s current decision making to adjust for the unmeasured confounding. We establish a novel identification result and propose a new off-policy evaluation (OPE) method for evaluating policy pairs in this two-player turn-based game. To tackle the distributional mismatch, we leverage the idea of pessimism and use our OPE method to develop an off-policy policy learning algorithm for finding a desirable policy pair for both Alice and Bob. Moreover, we prove that under some technical assumptions, the policy pair obtained through our method converges to the optimal one at a satisfactory rate. Finally, we conduct a simulation study to demonstrate the performance of the proposed method.This paper was accepted by Nicolas Stier, Special Issue on the Human-Algorithm Connection.Funding: L. Wang’s research is partially supported by the National Science Foundation [Grant FRGMS-1952373].Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.04112 .

Learning to Be Fair: A Consequentialist Approach to Equitable Decision Making

2026-01-14T16:08:19+00:00

Management Science
In an attempt to make algorithms fair, the machine learning literature has largely focused on equalizing decisions, outcomes, or error rates across race or gender groups. To illustrate, consider a hypothetical government rideshare program that provides transportation assistance to low-income people with upcoming court dates. Following this literature, one might allocate rides to those with the highest estimated treatment effect per dollar while constraining spending to be equal across race groups. That approach, however, ignores the downstream consequences of such constraints and, as a result, can induce unexpected harm. For instance, if one demographic group lives farther from court, enforcing equal spending would necessarily mean fewer total rides provided and potentially more people penalized for missing court. Here we present an alternative framework for designing equitable algorithms that foregrounds the consequences of decisions. In our approach, one first elicits stakeholder preferences over the space of possible decisions and the resulting outcomes—such as preferences for balancing spending parity against court appearance rates. We then optimize over the space of decision policies, making trade-offs in a way that maximizes the elicited utility. To do so, we develop an algorithm for efficiently learning these optimal policies from data for a large family of expressive utility functions. In particular, we use a contextual bandit algorithm to explore the space of policies while solving a convex optimization problem at each step to estimate the best policy based on the available information. This consequentialist paradigm facilitates a more holistic approach to equitable decision making.This paper was accepted by Catherine Tucker, Special Issue on the Human-Algorithm Connection.Funding: This work was supported by Stanford Impact Labs [Start-Up Impact Labs/GEJBU], the Stanford Institute for Human-Centered Artificial Intelligence, and Harvard Data Science Initiative.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00345 .

Reskilling the Workforce for AI: Domain Expertise and Algorithmic Literacy

2026-01-14T16:08:19+00:00

Management Science
This study provides evidence that AI and algorithms act as complements to domain expertise, creating the greatest value when algorithmic literacy is broadly diffused among workers. Unlike earlier business technologies that concentrated expertise in IT specialists, AI and algorithms are most effective when domain experts themselves can interpret and apply them. Using two workforce datasets, I show that demand for algorithmic skills is rising among domain experts, frontier firms diffuse these skills broadly, and markets reward firms’ AI and algorithmic investments more when such capabilities are dispersed. The spread of no-code and natural language tools accelerates this shift by lowering barriers to use and allowing domain experts to integrate algorithms into their decision-making processes. These patterns underscore the importance of workforce training and organizational design in realizing productivity gains from AI adoption.This paper was accepted by D. J. Wu, Special Issue on the Human-Algorithm Connection.Funding: The Wharton Mack Institute provided financial assistance.Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.03968 .

Human–Algorithmic Bias: Source, Evolution, and Impact

2026-01-14T16:08:19+00:00

Management Science
Prior work on human-algorithmic bias has seen difficulty in empirically identifying the underlying mechanisms of bias because in a typical “one-time” decision-making scenario, different mechanisms generate the same patterns of observable decisions. In this study, leveraging a unique repeat decision-making setting in a high-stakes microlending context, we aim to uncover the underlying source, evolution dynamics, and associated impacts of bias. We first develop a structural econometric model of the decision dynamics to understand the source and evolution of bias in human evaluators in microloan granting. We find that both preference-based and belief-based biases exist in human decisions and are in favor of female applicants. Our counterfactual simulations show that the elimination of either of the two biases improves the fairness in financial resource allocation as well as the platform profits. The profit improvement mainly stems from the increased approval probability for male borrowers, especially those who would eventually pay back loans. Furthermore, to examine how human biases evolve when being inherited by machine learning (ML) algorithms, we train state-of-the-art ML algorithms for default risk prediction on both real-world data sets with human biases encoded within and counterfactual data sets with human biases partially or fully removed. We find that even fairness-unaware ML algorithms can reduce bias in human decisions. Interestingly, although removing both types of human bias from the training data can further improve ML fairness, the fairness-enhancing effects vary significantly between new and repeat applicants. Based on our findings, we discuss how to reduce decision bias most effectively in a human-ML pipeline.This paper was accepted by D. J. Wu, Special Issue on the Human-Algorithm Connection.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03862 .

On Statistical Discrimination as a Failure of Social Learning: A Multiarmed Bandit Approach

2026-01-14T16:08:19+00:00

Management Science
We analyze statistical discrimination in hiring markets using a multiarmed bandit model. Myopic firms face workers arriving with heterogeneous observable characteristics. The association between the worker’s skill and characteristics is unknown ex ante; thus, firms need to learn it. Laissez-faire causes perpetual underestimation: minority workers are rarely hired, and therefore, the underestimation tends to persist. Even a marginal imbalance in the population ratio frequently results in perpetual underestimation. We demonstrate that a subsidy rule that is implemented as temporary affirmative action effectively alleviates discrimination stemming from insufficient data.This paper was accepted by Nicolas Stier-Moses, Special Issue on the Human-Algorithm Connection.Funding: This work was supported by the Social Sciences and Humanities Research Council of Canada [Grant 430-2020-00088] and JST ERATO [Grant JPMJER2301], Japan.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00893 .

Algorithm Aversion: Evidence from Ridesharing Drivers

2026-01-14T16:08:19+00:00

Management Science
The low rate of adoption by human users often hinders AI algorithms from achieving their intended efficiency gains. This is particularly true for algorithms that prioritize system-wide objectives because they can create misalignment of incentives and cause confusion among potential users. We provide one of the first large-scale field studies on algorithm aversion by leveraging an algorithmic recommendation rollout on a large ridesharing platform. We identify contextual experience and herding as two important factors that explain ridesharing drivers’ aversion to an algorithm that is designed to help drivers make better location choices. Specifically, we find that drivers are less likely to follow the algorithm when the algorithmic recommendation does not align with their past experience at a given location-time unit and when their peers’ actions contradict the algorithmic recommendations. We discuss the managerial implications of these findings.This paper was accepted by Catherine Tucker, Special Issue on the Human-Algorithm Connection.Funding: The research at Shanghai Jiaotong University was supported by the National Natural Science Foundation of China [Grants 72202135, 72110107001, 72231003]. S. Zhang acknowledges the support of Shanghai Pujiang Program [Grant 21PJC070], and Special Fund for Creative Research Groups [Grant 72221001]. Y. Zhu acknowledges the support of National University of Singapore [Grant WBS A-8000489-00-00].Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.02475 .

Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence

2026-01-14T16:08:19+00:00

Management Science
We run an online experiment to study the origins of algorithm aversion. Participants are in the role of either workers or managers. Workers perform three real-effort tasks: task 1, task 2, and the job task, which is a combination of tasks 1 and 2. They choose whether the hiring decision between themselves and another worker is made by a participant in the role of a manager or by an algorithm. In a second set of experiments, managers choose whether they want to delegate their hiring decisions to the algorithm. When the algorithm does not use workers’ gender to predict their job-task performance and workers know this, they choose the algorithm more often than in the baseline treatment where gender is employed. Feedback to the managers about their performance in hiring the best workers increases their preference for the algorithm relative to the baseline without feedback, because managers are, on average, overconfident. Finally, providing details on how the algorithm works does not increase the preference for the algorithm for workers or for managers.This paper was accepted by Elena Katok, Special Issue on the Human-Algorithm Connection.Funding: D. Kübler acknowledges financial support from the Deutsche Forschungsgemeinschaft [CRC TRR 190], R. Hakimov acknowledges financial support from the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung [Project 100018_189152], and M.-P. Dargnies acknowledges financial support from the Agence Nationale de la Recherche (ANR JCJC TrustSciTruths).Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02774 .

Profit Implications of Judgmental Adjustments to Forecast Inputs: Evidence from a Large-Scale Field Experiment

2026-01-14T16:08:19+00:00

Management Science
In this paper, we report the results from a large-scale field experiment at a spare parts retail chain that considers whether allowing merchants to override forecast inputs to an inventory algorithm improves profits. Although the judgmental forecasting literature has studied extensively whether judgmental adjustments improve forecast performance, causal empirical evidence is missing in regard to whether judgmental adjustments improve bottom-line profits. Our results show that judgmental adjustments to the forecast input increase profitability by 4.92% on average compared with relying on automation without human intervention. We find that the well-established motivation-opportunity-ability framework provides clear insight into when judgmental adjustments improve profits, by examining heterogeneity in our data regarding stock-keeping unit margin, lifecycle, and size of supplier. Our data set also allows for examining both forecast accuracy and profits. We empirically support the wisdom from the judgmental forecasting literature that forecast performance need not translate to profit performance, calling attention to the need to consider operational performance beyond forecast accuracy as an end in itself.This paper was accepted by Felipe Caro, Special Issue on the Human-Algorithm Connection.Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2024.06321 .

Algorithmic Precision and Human Decision: A Study of Interactive Optimization for School Schedules

2026-01-14T16:08:19+00:00

Management Science
In collaboration with the San Francisco Unified School District (SFUSD), this paper introduces an interactive optimization framework to tackle complex school scheduling challenges. The choice of school start and end times is an optimization challenge, as schedules influence the district’s transportation system, and limiting the associated costs is a computationally difficult combinatorial problem. However, it is also a policy challenge, as transportation costs are far from the only consequence of school schedule changes. Policymakers need time and knowledge to balance these considerations and reach a consensus carefully; past implementations have failed because of policy issues, despite state-of-the-art optimization approaches. We first motivate our approach with a microfoundation model of the interplay between policymakers and researchers, arguing that limiting their dependency is key. Building on these insights, we propose a framework that includes (1) a fast algorithm capable of solving the school schedule problem that compares favorably to the literature and (2) an interactive optimization approach that leverages this speed to allow policymakers to explore a variety of solutions in a transparent and efficient way, facilitating the policy decision-making process. The framework led to the first optimization-driven school start time changes in the United States, updating the schedule of all 133 schools in SFUSD in 2021, with annual transportation savings exceeding $5 million. A comprehensive survey of approximately 27,000 parents and staff in 2022 provides evidence of the approach’s effectiveness.This paper was accepted by Felipe Caro, Special Issue on the Human-Algorithm Connection.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05834 .

Trading Gamification and Investor Behavior

2026-01-14T16:08:19+00:00

Management Science
We study the effect of gamification on retail traders’ behavior using a randomized online experiment. Participants with lower financial literacy prefer platforms with hedonic gamification elements, such as confetti and achievement badges. On average, hedonic gamification increases trading volume by 5.17%. However, the difference in trading activity between gamified and nongamified platforms is driven primarily by self-selection (70%) rather than gamification (30%). Participants who prefer hedonic gamification exhibit noisy trading strategies, whereas those favoring nongamified platforms display stronger contrarian behavior. Further, price trend notifications enhance learning for investors with accurate beliefs, but they reinforce trading mistakes for those with incorrect beliefs.This paper was accepted by Jean-Edouard Colliard, Special Issue on the Human-Algorithm Connection.Funding: P. Chapkovski acknowledges funding from the Deutsche Forschungsgemeinschaft [Germany’s Excellence Strategy—EXC 2126/1-390838866]. M. Khapko and M. Zoican acknowledge the Social Sciences and Humanities Research Council of Canada [Insight Development Grant 430-2018-00125] and the Canadian Securities Institute Research Foundation [research grant]. M. Zoican acknowledges financial support from the Quantitative Management Research Initiative (QMI) under the aegis of the Fondation du Risque, a joint initiative by Université Paris-Dauphine, l’École Nationale de la Statistique et de l’Administration ParisTech, and LFIS, France.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02650 .

Human-Algorithm Collaboration with Private Information: Naïve Advice-Weighting Behavior and Mitigation

2026-01-14T16:08:19+00:00

Management Science
Even if algorithms make better predictions than humans on average, humans may sometimes have private information that an algorithm does not have access to that can improve performance. How can we help humans effectively use and adjust recommendations made by algorithms in such situations? When deciding whether and how to override an algorithm’s recommendations, we hypothesize that people are biased toward following naïve advice-weighting (NAW) behavior; they take a weighted average between their own prediction and the algorithm’s prediction, with a constant weight across prediction instances regardless of whether they have valuable private information. This leads to humans overadhering to the algorithm’s predictions when their private information is valuable and underadhering when it is not. In an online experiment where participants were tasked with making demand predictions for 20 products while having access to an algorithm’s predictions, we confirm this bias toward NAW and find that it leads to a 20%–61% increase in prediction error. In a second experiment, we find that feature transparency—even when the underlying algorithm is a black box—helps users more effectively discriminate how to deviate from algorithms, resulting in a 25% reduction in prediction error. We make further improvements in a third experiment via an intervention designed to move users away from advice weighting and instead, use only their private information to inform deviations, leading to a 34% reduction in prediction error.This paper was accepted by Elena Katok, Special Issue on the Human-Algorithm Connection.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03850 .

The Fairness of Credit Scoring Models

2026-01-14T16:08:19+00:00

Management Science
In credit markets, screening algorithms aim to discriminate between good-type and bad-type borrowers. However, when doing so, they can also discriminate between individuals sharing a protected attribute (e.g., gender, age, racial origin) and the rest of the population. This can be unintentional and originate from the training data set or from the model itself. We show how to formally test the algorithmic fairness of scoring models and how to identify the variables responsible for any lack of fairness. We then use these variables to optimize the fairness-performance tradeoff. Our framework provides guidance on how algorithmic fairness can be monitored by lenders, controlled by their regulators, improved for the benefit of protected groups, while still maintaining a high level of forecasting accuracy.This paper was accepted by Will Cong, Special Issue on the Human-Algorithm Connection.Funding: This work was supported by the Autorité de Contrôle Prudentiel et de Résolution (ACPR) Chair in Regulation and Systemic Risk, the Fintech Chair at Dauphine-PSL University, and the French National Research Agency (ANR) [MLEforRisk ANR-21-CE26-0007, Ecodec ANR-11-LABX-0047, and F-STAR ANR-17-CE26-0007-01].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03888 .

Engaging Customers with AI in Online Chats: Evidence from a Randomized Field Experiment

2026-01-14T16:08:19+00:00

Management Science
We examine how artificial intelligence (AI) affected the productivity of customer service agents and customer sentiment in online interactions. Collaborating with a meal delivery company, we conducted a randomized field experiment that exploited exogenous variation in giving agents access to AI-generated suggestions. We found that AI improved both the efficiency and effectiveness of the interactions: AI-assisted agents responded faster, engaged customers more deeply, and achieved greater improvements in customer sentiment. The benefits were most pronounced for less-experienced agents. However, AI’s impact varied by conversation type: It improved efficiency and customer sentiment in subscription cancellation requests but was the least effective in repeat complaint scenarios because of systemic issues beyond the AI’s capability. A text analysis of agent messages suggests that improved customer sentiment was explained by AI-assisted agents exhibiting higher levels of key response characteristics: empathy, information, and solution. Furthermore, we exploit a unique data feature: Customers first chatted with an automated chatbot without any human intervention before they were transferred to human agents (who may or may not have had AI assistance). We found that if customers who had experienced chatbot comprehension failures were then connected to AI-assisted human agents, the involvement of AI negatively affected customer sentiment. This is because unusually rapid responses in the latter scenario led customers to believe they were still communicating with a chatbot only, suggesting a spillover from their initial negative chatbot experiences. Companies should understand the conversation contexts, such as customer intent and chatbot interactions, when integrating AI into their customer support strategies.This paper was accepted by Catherine Tucker, Special Issue on the Human-Algorithm Connection.Funding: This research was supported by funding provided by Harvard Business School.Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.03920 .

Identity Disclosure and Anthropomorphism in Voice Chatbot Design: A Field Experiment

2026-01-14T16:08:19+00:00

Management Science
Fueled by the widespread adoption of algorithms and artificial intelligence, the use of chatbots has become increasingly popular in various business contexts. In this paper, we study how to effectively and appropriately use voice chatbots, particularly by leveraging the two design features identity disclosure and anthropomorphism, and evaluate their impact on the firm operational performance. In collaboration with a large truck-sharing platform, we conducted a field experiment that randomly assigned 11,000 truck drivers to receive outbound calls from the voice chatbot dispatcher of our focal platform. Our empirical results suggest that disclosing the identity of the chatbot at the beginning of the conversation negatively affects operational performance, leading to around 11% reduction in the response probability. However, humanizing the voice chatbot by adding our proposed anthropomorphism features (i.e., interjections and filler words) significantly improves response probability, conversation length, and the probability of order acceptance intention by over 5.6%, 24.9%, and 10.1%, respectively. Moreover, even when the chatbot’s identity is disclosed along with humanizing features, the operational outcomes still improve. This finding suggests that enhancing anthropomorphism may potentially counteract the negative effects of chatbot identity disclosure. Finally, we propose one plausible explanation for the performance improvement—the enhanced trust between humans and algorithms—and provide empirical evidence that drivers are more likely to disclose information to chatbot dispatchers with anthropomorphism features. Our proposed anthropomorphism improvement solutions are currently being implemented and utilized by our collaborator platform.This paper was accepted by Felipe Caro, Special Issue on the Human-Algorithm Connection.Funding: This study is supported by the National Natural Science Foundation of China [Grants 72172169 and 91646125], Program for Innovation Research at the Central University of Finance and Economic, and Shanghai Pujiang Program.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03833 .

Roles of Artificial Intelligence in Collaboration with Humans: Automation, Augmentation, and the Future of Work

2026-01-14T16:08:19+00:00

Management Science
Humans will see significant changes in the future of work as collaboration with artificial intelligence (AI) will become commonplace. This work explores the benefits of AI in the setting of judgment tasks when it replaces humans (automation) and when it works with humans (augmentation). Through an analytical modeling framework, we show that the optimal use of AI for automation or augmentation depends on different types of human-AI complementarity. Our analysis demonstrates that the use of automation increases with higher levels of between-task complementarity. In contrast, the use of augmentation increases with higher levels of within-task complementarity. We integrate both automation and augmentation roles into our task allocation framework, where an AI and humans work on a set of judgment tasks to optimize performance with a given level of available human resources. We validate our framework with an empirical study based on experimental data in which humans classify images with and without AI support. When between-task complementarity and within-task complementarity exist, we see a consistent distribution of work pattern for optimal work configurations; AI automates relatively easy tasks, AI augments humans on tasks with similar human and AI performance, and humans work without AI on relatively difficult tasks. Our work provides several contributions to theory and practice. The findings on the effects of complementarity provide a nuanced view regarding the benefits of automation and augmentation. Our task allocation framework highlights potential job designs for the future of work, especially by considering the often-ignored, critical role of human resource reallocation in improving organizational performance.This paper was accepted by D. J. Wu, Special Issue on the Human-Algorithm Connection.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05684 .

The Power of Disagreement: A Field Experiment to Investigate Human–Algorithm Collaboration in Loan Evaluations

2026-01-14T16:08:19+00:00

Management Science
Human–algorithm collaboration is becoming increasingly prevalent in the economy and society. However, this collaboration is not always fruitful, and in extreme cases, people become human borgs or totally averse to algorithms. The key to collaborative value is whether humans and algorithms can complement each other in decision making, but it is challenging for humans to disagree with algorithmic recommendations at the right time (i.e., to disagree when algorithms are wrong and not disagree when algorithms are right). To understand the centric role of disagreement in human–algorithm collaboration and examine when and how it benefits, we conducted a field experiment in which human evaluators and algorithms worked together to evaluate loan applications under four scenarios, that is, limited/rich information and with/without disclosure of algorithm rationale. Our results show that human–algorithm collaboration decisions outperformed human- or algorithm-only decisions, and these collaborative values varied across the four scenarios. We further propose a theoretical framework for mechanism examination that centers on the formation and effectiveness of disagreement. We validate the framework empirically and come to the following findings: (1) disagreement exhibits a sizable and nonlinear influence on collaborative value, (2) the differences between human evaluators and algorithms in decision making contribute to disagreement but not to collaborative value, (3) algorithm self-contradiction triggers disagreement and helps human evaluators disagree with algorithms at the right time. These findings provide valuable theoretical insights on how collaborative value is achieved and managerial insights on how to manage disagreement in human–algorithm collaboration.This paper was accepted by Hemant Bhargava, Special Issue on the Human-Algorithm Connection.Funding: Y. Zhang appreciates the support from the National Natural Science Foundation of China [Grant 72272003].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03844 .

Digital Lyrebirds: Experimental Evidence That Voice-Based Deep Fakes Influence Trust

2026-01-14T16:08:19+00:00

Management Science
We consider the pairing of audio chatbot technologies with voice-based deep fakes, that is, voice clones, examining the potential of this combination to induce consumer trust. We report on a set of controlled experiments based on the investment game, evaluating how voice cloning and chatbot disclosure jointly affect participants’ trust, reflected by their willingness to play with an autonomous, AI-enabled partner. We observe evidence that voice-based agents garner significantly greater trust from subjects when imbued with a clone of the subject’s voice. Recognizing that these technologies present not only opportunities but also the potential for misuse, we further consider the moderating impact of AI disclosure, a recent regulatory proposal advocated by some policymakers. We find no evidence that AI disclosure attenuates the trust-inducing effect of voice clones. Finally, we explore underlying mechanisms and contextual moderators for the trust-inducing effects, with an eye toward informing future efforts to manage and regulate voice-cloning applications. We find that a voice clone’s effects operate, at least in part, by inducing a perception of homophily and that the effects are increasing in the clarity and quality of generated audio. Implications of these results for consumers, policymakers, and society are discussed.This paper was accepted by D. J. Wu, Special Issue on the Human-Algorithm Connection.Funding: This work was supported by funding from the University of Wisconsin-Milwaukee Research Assistance Fund.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03316 .

Human-Robot Interactions in Investment Decisions

2026-01-14T16:08:19+00:00

Management Science
We study the introduction of robo-advising on a large set of employee saving plans. Different from many services that fully automate portfolio decisions, our robo-advisor proposes investment and rebalancing strategies, leaving investors free to follow or ignore them. The resulting human-robot interactions occur both at the time of the subscription and over time, as the robot sends alerts when the investor’s portfolio gets too far from the target allocation. We show that the robo-service is associated with an increase in investors’ attention and trading activities. Following the robot’s alerts, investors change their rebalancing behaviors so as to stay closer to their target allocation, which results in larger portfolio returns. Counterfactual returns induced by automatic rebalancing by the robot would be only slightly higher, suggesting that, on average, the financial cost of letting investors retain control is not large.This paper was accepted by Jean-Edouard Colliard, Special Issue on the Human-Algorithm Connection.Funding: This work was supported by Observatoire epargne europeenne, as well as LTI@Unito and Agence Nationale de la Recherche [Grant ANR-17-EURE-0010] to M. Bianchi.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03886 .

Artificial Intelligence, Lean Startup Method, and Product Innovations

2026-01-14T16:08:19+00:00

Management Science
Although artificial intelligence (AI) has the potential to drive significant business innovation, many firms struggle to realize its benefits. We investigate why some firms succeed in using AI for innovation, whereas others fail, focusing on the organizational support necessary for leveraging AI in both novel and incremental innovation. Specifically, we examine how the lean startup method (LSM) influences the impact of AI on product innovation in startups. Analyzing data from 1,800 Chinese startups between 2011 and 2020, alongside policy shifts by the Chinese government in encouraging AI adoption, we find that companies with strong AI capabilities produce more innovative products. Moreover, our study reveals that AI investments complement LSM in innovation, with effectiveness varying by the type of innovation and AI capability. We differentiate between discovery-oriented AI, which reduces uncertainty in novel areas of innovation, and optimization-oriented AI, which refines and optimizes existing processes. Within the framework of LSM, we further distinguish between prototyping—focused on developing minimum viable products—and controlled experimentation—focused on rigorous testing such as A/B testing. We find that LSM complements discovery-oriented AI by utilizing AI to expand the search for market opportunities and employing prototyping to validate these opportunities, thereby reducing uncertainties and facilitating the development of the first release of products. Conversely, LSM complements optimization-oriented AI by using A/B testing to experiment with the universe of input features and using AI to streamline iterative refinement processes, thereby accelerating the improvement of iterative releases of products. As a result, when firms use AI and LSM for product development, they are able to generate more high-quality products in less time. These findings, applicable to both software and hardware development, underscore the importance of treating AI as a heterogeneous construct because different AI capabilities require distinct organizational processes to achieve optimal outcomes.This paper was accepted by D. J. Wu, Special Issue on the Human-Algorithm Connection.Funding: Financial support from the Mack Institute for Innovation Management is gratefully acknowledged.Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.03905 .

Is Your Machine Better Than You? You May Never Know

2026-01-14T16:08:19+00:00

Management Science
Artificial intelligence systems are increasingly demonstrating their capacity to make better predictions than human experts. Yet recent studies suggest that professionals sometimes doubt the quality of these systems and overrule machine-based prescriptions. This paper explores the extent to which a decision maker (DM) supervising a machine to make high-stakes decisions can properly assess whether the machine produces better recommendations. To that end, we study a setup in which a machine performs repeated decision tasks (e.g., whether to perform a biopsy) under the DM’s supervision. Because stakes are high, the DM primarily focuses on making the best choice for the task at hand. Nonetheless, as the DM observes the correctness of the machine’s prescriptions across tasks, the DM updates the DM’s belief about the machine. However, the DM is subject to a so-called verification bias such that the DM verifies the machine’s correctness and updates the DM’s belief accordingly only if the DM ultimately decides to act on the task. In this setup, we characterize the evolution of the DM’s belief and overruling decisions over time. We identify situations under which the DM hesitates forever whether the machine is better; that is, the DM never fully ignores but regularly overrules it. Moreover, the DM sometimes wrongly believes with positive probability that the machine is better. We fully characterize the conditions under which these learning failures occur and explore how mistrusting the machine affects them. These findings provide a novel explanation for human–machine complementarity and suggest guidelines on the decision to fully adopt or reject a machine.This paper was accepted by Elena Katok, Special Issue on the Human-Algorithm Connection.Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4791 .

Strategic Responses to Algorithmic Recommendations: Evidence from Hotel Pricing

2026-01-14T16:08:19+00:00

Management Science
We study the interaction between algorithmic advice and human decisions using high-resolution hotel-room pricing data. We document that price setting frictions, arising from adjustment costs of human decision makers, induce a conflict of interest with the algorithmic advisor. A model of advice with costly price adjustments shows that, in equilibrium, algorithmic price recommendations are strategically biased and lead to suboptimal pricing by human decision makers. We quantify the losses from the strategic bias in recommendations using as structural model and estimate the potential benefits that would result from a shift to fully automated algorithmic pricing.This paper was accepted by Axel Ockenfels, Special Issue on the Human-Algorithm Connection.Funding: D. Garcia gratefully acknowledges that this research was funded in part by the Austrian Science Fund [Grant FWF-FG6]. A. K. Wagner gratefully acknowledges financial support from the Anniversary Fund of the Oesterreichische Nationalbank [Project 18878].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03740 .

The Gatekeeper Effect: The Implications of Pre-Screening, Self-Selection, and Bias for Hiring Processes

2026-01-14T16:08:19+00:00

Management Science
We study the problem of screening in decision-making processes under uncertainty, while focusing on the impact of adding an additional screening stage, commonly known as a “gatekeeper.” Although our main analysis is rooted in the context of job market hiring, the principles and findings are broadly applicable to areas such as educational admissions, patient healthcare selection, and financial loan approvals. The gatekeeper’s role is to assess applicant suitability before significant costs are incurred. Our study reveals that although gatekeepers are designed to streamline selection processes by filtering out the candidates who are less likely to be selected, sometimes they inadvertently affect the candidate’s own decision-making process. We explore the conditions under which the introduction of a gatekeeper can enhance or impede the efficiency of these processes. Additionally, we consider how gatekeeping strategies can be adapted to influence the accuracy of selection decisions. Our research also extends to scenarios in which gatekeeping is influenced by historical biases, particularly in competitive settings like hiring. We discover that candidates confronted with a statistically biased gatekeeping process are more likely to withdraw from the job application process, thereby perpetuating the previously mentioned historical biases. The study suggests that measures such as affirmative action can effectively address these biases. Although centered on hiring, the insights and methodologies from our study have significant implications for a wide range of fields to which screening and gatekeeping are integral.This paper was accepted by Nicolas Stier-Moses, Special Issue on the Human-Algorithm Connection.Funding: Financial support from the Center of Mathematical Sciences and Applications (CMSA) at Harvard University and the Ministry of Science and Technology of Israel (Yitzhak Shamir Fellowship) is gratefully acknowledged.

Can Artificial Intelligence Improve Gender Equality? Evidence from a Natural Experiment

2026-01-14T16:08:19+00:00

Management Science
Gender discrimination in education hinders women’s representation in various fields. How can we create a gender-neutral learning environment when teachers’ gender composition and mindset are slow to change? Recent development in artificial intelligence (AI) provides a way to achieve this goal as engineers can make AI trainers gender neutral and not take gender-related information as input. We use data from a natural experiment in which such AI trainers replace some human teachers for a male-dominated strategic board game to test the effectiveness of AI training. The introduction of AI improves teaching outcomes for boys and girls and reduces the preexisting gender gap. Survey responses indicate that AI’s information advantage, friendly appearance, and interactive features helped students to learn faster, and class recordings suggest that AI trainers’ nondiscriminatory emotional status can explain the improvement in gender equality. We demonstrate AI’s potential in improving learning outcomes and promoting diversity, equity, and inclusion in analogous settings.This paper was accepted by Elena Katok, Special Issue on the Human-Algorithm Connection.Funding: D. Huang gratefully acknowledges financial support from the National Natural Science Foundation of China [Grants 72503232, 71988101, and T2293771]. C. Lin gratefully acknowledges financial support from the National Natural Science Foundation of China [Grant 72192841] and the Research Grants Council of the Hong Kong Special Administration Region, China [Project No. T35/710/20R].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02787 .

Algorithm Reliance: Fast and Slow

2026-01-14T16:08:19+00:00

Management Science
In algorithm-augmented service contexts where workers have decision authority, they face two decisions about the algorithm: whether to follow its advice and how quickly to do so. The pressure to work quickly increases with the speed of arriving customers. In this paper, we ask the following. How do workers use algorithms to manage system loads? With a laboratory experiment, we find that superior algorithm quality and high system loads increase participants’ willingness to use their algorithm’s advice. Consequently, participants with the superior algorithm make higher-quality recommendations than those with no algorithm (participants with the inferior algorithm make slightly lower-quality recommendations than those without). However, participants do not necessarily speed up by using algorithms’ advice; their throughput times only decrease compared with the no-algorithm baseline when the system load is high and algorithm quality is superior, although participants would benefit from working faster in all treatments. This happens in part because participants in the high-load, superior-algorithm treatment serve customers more quickly than participants in the other treatments, conditional on using the algorithm. Participants in the high-load, superior-algorithm treatment work especially quickly in later periods as they increasingly default to their algorithm’s advice. Our findings show that algorithms can have benefits for both decision quality and speed. Quality benefits come from workers’ decision to use their algorithms’ advice, whereas speed benefits depend on workers’ algorithm use and the time they spend deliberating about their algorithm use. Ultimately, algorithm quality and system load are mutually reinforcing factors that influence both service quality and especially speed.This paper was accepted by Elena Katok, Special Issue on the Human-Algorithm Connection.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01989 .

Gender-Biased Technological Change: Milking Machines and the Exodus of Women from Farming

2026-01-01T00:00:00+00:00

American Economic Review
This paper studies how gender-biased technological change in agriculture affected women’s work in twentieth-century Norway. In the 1950s, dairy farms began widely adopting milking machines to replace milking cows by hand, a task typically performed by young women. We show that the machines pushed rural young women in dairy-intensive areas out of farming. The displaced women moved to cities where they acquired more education and found better-paying, skilled employment. Our results suggest that the adoption of milking machines broke up allocative inefficiencies associated with moving costs across sectors, which improved the economic status of women relative to men. (JEL J16, J24, J43, J61, N34, N54, O33)

Tying with Network Effects

2026-01-01T00:00:00+00:00

American Economic Review
We develop a leverage theory of tying in markets with network effects. When a monopolist in one market cannot perfectly extract surplus from consumers, tying can be a mechanism through which unexploited consumer surplus is used as a demand-side leverage to create a “quasi-installed base” advantage in another market characterized by network effects. Our mechanism does not require any precommitment to tying; rather, tying emerges as a best response that lowers the quality of tied-market rivals. While tying can lead to exclusion of tied-market rivals, it can also expand use of the tying product, leading to ambiguous welfare effects. (JEL D41, D85, K21, L15, L40)

Sequential Learning under Informational Ambiguity

2026-01-01T00:00:00+00:00

American Economic Review
This paper investigates a sequential social learning problem in which individuals face ambiguity about others’ signal structures and have max-min expected utility preferences, thereby exhibiting ambiguity aversion. Unlike previous findings, which suggest that learning outcomes depend on the specifics of the learning environment, this study establishes information cascades as a robust outcome under ambiguity. With sufficient ambiguity, cascades arise almost surely, regardless of the statistical properties of signal structures. Moreover, standard results predicting the absence of cascades can easily break down: Even minimal ambiguity can trigger cascades when signals are bounded and lead to incorrect herding when signals are unbounded. (JEL D81, D82, D83)

Racial Disparities in Housing Returns

2026-01-01T00:00:00+00:00

American Economic Review
We show that higher rates of distressed home sales (i.e., foreclosures and short sales) among Black and Hispanic homeowners severely reduce realized housing returns for these groups—in particular, to a level below that realized by White homeowners. Yet absent financial distress, houses owned by minorities do not appreciate at substantially slower rates than houses owned by nonminorities. Racial differences in liquidity and income stability, which are imperfectly observed by lenders, are important determinants of differences in distress. Policies that prevent foreclosure among distressed minorities can mitigate the racial gap in returns. (JEL D31, G51, J15, R31)

Optimal Taxation and Market Power

2026-01-01T00:00:00+00:00

American Economic Review
Should optimal income taxation change when firms have market power? We analyze how the planner can optimally tax labor income of workers and profits of entrepreneurs. We derive optimal tax rates that depend on markups and identify four distinct components: the Mirrleesian incentive effect, the Pigouvian tax correction of the negative externality of market power, redistribution through altered factor prices, and reallocation of output toward the most productive firms. We quantify the optimal tax for the US economy and provide concrete proposals how to use income taxation to redistribute income while incentivizing production in the presence of market power. (JEL D24, D31, D43, H21, H23, H24, H25)

Risk Preferences and Field Behavior: The Relevance of Higher-Order Risk Preferences

2026-01-01T00:00:00+00:00

American Economic Review
Using new methods, we measure the intensities of higher-order risk preferences (prudence and temperance) in an incentivized experiment with 658 adolescents. Aligned with theory, we find that higher-order risk preferences are strongly related to field behavior, including prevention, health, addictive behavior, and financial decision-making. Most importantly, we show that ignoring prudence and temperance can yield misleading conclusions about the relation of risk preferences to field behavior, and that survey measures of risk tolerance often relate to field behavior because they capture higher-order risk preferences. (JEL C83, D81, D91, J13)

Across-Country Wage Compression in Multinationals

2026-01-01T00:00:00+00:00

American Economic Review
Many employers link wages at establishments outside of the home region to the level at headquarters. We show this using new data on 1,200 multinationals’ establishments across the world and linked employee-level data on their establishments in Brazil. Headquarters wage changes arising from minimum wage and exchange rate shocks are partially transmitted to workers employed in the same position abroad. Wage change transmission appears to be direct and results from firm-wide wage-setting procedures rather than associated technology or employment changes. “Anchored” wage setting is somewhat associated with particular characteristics of the job × employer × headquarters-establishment country-pair. (JEL F23, F31, J24, J31, J38, M16, O15)

Monetary Cooperation during Global Inflation Surges

2026-01-01T00:00:00+00:00

American Economic Review
We study optimal monetary policy during times of global scarcity of tradable goods. The optimal monetary response entails a surge in inflation, which helps rebalance production toward the tradable sector. While the inflation costs are fully borne domestically, however, the gains in terms of higher supply of tradable goods partly spill over to the rest of the world. National central banks may thus fall into a coordination trap and implement an excessively tight monetary policy causing an unnecessarily sharp global contraction. (JEL E24, E31, E32, E52, F11, F31, F42)

The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility

2026-01-01T00:00:00+00:00

American Economic Review
We construct a public atlas of mean outcomes in adulthood by childhood census tract. Outcomes vary sharply across neighborhoods: For children whose parents earn $27,000, the standard deviation of mean household income in adulthood is $10,420 across tracts within counties. Only half the variation in outcomes is explained by traditional measures of neighborhood opportunity like poverty rates. Experimental and quasi-experimental estimates indicate 60 percent of the variation in outcomes across neighborhoods is driven by causal effects. We demonstrate how our statistics can be applied to better target policies to improve low-opportunity areas and help families move to affordable high-opportunity areas. (JEL G51, I32, I38, J12, R23)

Monotonicity among Judges: Evidence from Judicial Panels and Consequences for Judge IV Designs

2026-01-01T00:00:00+00:00

American Economic Review
Judge IV designs rely on monotonicity—each judge being weakly stricter than more lenient judges in all cases. I measure monotonicity in judicial panels in five different settings and find that it is violated in up to 50 percent of nonunanimous cases. The monotonicity violations are not detected by conventional tests, but they would typically induce little bias in judge IV estimates. (JEL C26, K41, K42, O17)

Strategic Capacity Commitment: A Channel Competition Perspective

2026-01-12T00:00:00+00:00

Marketing Science
This paper examines how capacity commitment shapes strategic interactions between asymmetric channels—one centralized and one decentralized—and reveals that a centralized manufacturer’s capacity commitment can mitigate price competition and benefit both channels.

EXPRESS: Giving Thanks: How Managers Should Respond to Compliments in Positive Word-of-Mouth

2026-01-12T00:00:00+00:00

Journal of Marketing
Consumers expect managers to respond to positive reviews, but it is unclear whether these responses are beneficial. This research finds that managerial responses to positive reviews can positively impact consumers when managers follow conversational norms for responding to compliments. It proposes that managers downplay the compliments that their firms receive via positive reviews (e.g., “Dinner was fantastic!”) and examines two norms-based strategies for doing so: 1) shift the content of the compliment (e.g., “We’re glad dinner was good.”) and 2) shift the recipient of the compliment (e.g., “…our suppliers helped.”). First, an experiment and Google Local data show a disconnect between how consumers think managers should respond and how managers currently respond. Second, six experiments test the proposed response strategies. Compared to managers that do not respond to positive reviews and to managers that write responses currently recommended by industry or academics, managers that downplay compliments improve readers’ evaluations of the firm and engagement on the platform. Downplaying the compliment improves consumer outcomes by conveying the manager’s humility, which is normative. Downplaying is most effective when the manager reduces credit to the firm, appreciates someone else involved (e.g., supplier or reviewer), and uses moderately positive descriptors (e.g., fantastic to good).

The Crash Risk in Individual Stocks Embedded in Skewness Swap Returns

2026-01-12T00:00:00+00:00

Journal of Financial and Quantitative Analysis
This article investigates crash risk premiums in individual stocks using skewness swaps. These swaps involve buying a stock’s risk-neutral skewness and receiving the realized skewness as a payoff. The strategy’s returns, which measure the skewness risk premium, are found to be consistently large and positive. This suggests investors are concerned about potential crashes in individual stocks and require substantial compensation for bearing this risk. Notably, significant results are mainly observed after the 2007/2009 financial crisis, indicating changes in post-crisis option market dynamics. Cross-sectional determinants of skewness swap returns include measures of systematic crash risk and stock overvaluation.

Human Capital Disclosure and Labor Market Outcomes: Evidence from Regulation S‐K

2026-01-12T00:00:00+00:00

Journal of Accounting Research
We examine the labor market consequences of the 2020 Regulation S‐K requiring human capital disclosure in 10K filings. Using large‐sample job‐level data and a Generative Large Language Model (GLLM), we observe that public firms subject to the regulation increase their disclosure of diversity, equity, and inclusion (DEI) information in job postings relative to a matched sample of large private firms. The increase in job‐posting disclosure is more pronounced among firms facing greater external pressure to increase their workforce diversity. These findings suggest a shift in demand for diverse candidates by public firms following the regulation. Yet, consistent with short‐term inelastic labor supply, this demand shift lengthens the recruitment period, with noticeable increases in workplace gender diversity emerging one year after the regulation, particularly among firms that demonstrate a credible commitment to DEI. Our study documents how securities regulations can impact labor market practices and underscores the challenges involved in shaping workforce diversity.

Workflow Automation in Open-Source Software Development: Accelerating Innovation Through Mechanization and Orchestration

2026-01-12T00:00:00+00:00

Information Systems Research
This study develops a conceptual framework distinguishing two mechanisms of workflow automation: mechanization and orchestration. Mechanization automates discrete, self-contained, repeatable tasks through standardized execution to enhance consistency, reliability, and efficiency, while orchestration automates the communication between tasks, workers, and stages, which facilitates information flow and coordination. We theorize that these mechanisms differentially affect incremental versus substantive innovation. Using a multimethod approach integrating machine learning and econometrics, we analyze the effects of workflow automation in open-source software development, demonstrating that mechanization accelerates maintenance-oriented exploitative innovation, whereas orchestration accelerates development-oriented explorative innovation. This mechanization-orchestration distinction extends beyond software contexts. For practitioners, aligning automation strategies with innovation goals is essential: deploy mechanization to enhance operational efficiency and support incremental improvements in stable environments; and implement orchestration to enable adaptive coordination in exploratory, high-velocity development requiring creativity and flexibility. For policymakers, understanding this distinction informs workforce development and technology adoption policies, as automation reshapes work by shifting human contribution from routine execution toward coordinated problem solving and strategic decision making.

Beyond feasibility filters: How expertise heterogeneity enables innovation recognition

2026-01-13T00:00:00+00:00

Strategic Management Journal
Organizations often struggle to identify promising innovations that balance novelty and feasibility in multidisciplinary domains, yet how does evaluator expertise heterogeneity shape these assessments? This study examines how evaluator expertise influences innovation evaluation through a field experiment with National Aeronautics and Space Administration's (NASA) Astrobee Robotic Arm Challenge, involving 354 evaluators assessing 101 solutions. Domain‐spanning evaluators assign higher novelty ratings while maintaining similar feasibility ratings compared to domain‐specific evaluators. Domain‐adjacent evaluators show higher ratings on both dimensions. Human‐LLM analysis of 3007 evaluator comments reveals a two‐stage process: feasibility filtering (evaluating minimum viability) followed by integrative assessment (evaluating enhancement potential). Different expertise types serve complementary functions: domain‐spanning evaluators recognize enhancement potential while maintaining rigorous standards; domain‐adjacent evaluators show openness to novel approaches; domain‐specific evaluators ensure technical rigor. These findings suggest effective innovation evaluation depends on strategically combining complementary expertise types rather than identifying optimal individual evaluators.Managerial SummaryOrganizations often struggle to identify innovations that are both novel and feasible, risking missed breakthroughs or wasted resources. We study how evaluator expertise shapes innovation assessments in a field experiment with NASA, in which 354 evaluators reviewed 101 robotic arm designs. Evaluators with expertise spanning multiple domains recognize more novel yet feasible ideas. Those with single‐domain expertise provide essential technical gatekeeping but overlook cross‐domain improvements, while adjacent‐field experts are more open but less rigorous. Organizations can strengthen innovation selection by strategically combining these complementary expertise types—using domain‐specific experts for initial feasibility screening and domain‐spanning experts to identify integrative opportunities—rather than seeking one “ideal” evaluator.

The Macroeconomics of Irreversibility

2026-01-13T00:00:00+00:00

Review of Economic Studies
We study aggregate capital dynamics in an investment model with idiosyncratic productivity shocks, fixed capital adjustment costs, and irreversibility driven by a wedge between capital purchase and resale prices. We derive sufficient statistics that capture the role of investment frictions in aggregate capital fluctuations, measure these statistics using investment microdata, and exploit them to discipline the capital price wedge. Irreversibility doubles the persistence of capital fluctuations and is crucial for reconciling micro-level investment behavior with macroeconomic propagation.

Growth Experiences and Trust in Government

2026-01-13T00:00:00+00:00

The Quarterly Journal of Economics
This paper explores the relationship between economic growth and trust in government using variation in GDP growth experienced over a lifetime since birth. We assemble a newly harmonized global dataset across eleven major opinion surveys, comprising 3.3 million respondents in 166 countries since 1990. Exploiting cohort-level variation, we find that individuals who experience higher GDP growth are more prone to trust their governments, with larger effects found in democracies. Higher growth experiences are also associated with improved perceptions of government performance and living standards. We find no similar channel between growth experience and interpersonal trust. Second, more recent growth experiences appear to matter most for trust in government, with no detectable effect of growth experienced during one’s formative years, closer to birth or before birth. Third, we find evidence of a “trust paradox” whereby average trust in government is lower in democracies than in autocracies. Our results are robust to a range of falsification exercises, robustness checks and single-country evidence using the American National Election Studies and the Swiss Household Panel.

EXPRESS: Impact of Server Capability on Pooling Configuration in Stochastic Service Systems

2026-01-13T00:00:00+00:00

Production and Operations Management
Resource pooling is often introduced in service systems to cope with the variability in customer demand. The primary motivation behind creating a more flexible system is to utilize resources efficiently—namely, assigning customers whose dedicated resources are fully occupied to available non-dedicated resources (referred to as off-service placement). In such a setup, the service system manager expects to serve more customers within a fixed timeframe. However, recent empirical evidence shows that, due to limited server capability, the service time at non-dedicated providers can be significantly longer than that at dedicated ones. In this study, we develop a two-server stochastic model to examine how server capability levels and other factors—such as overall workload and demand asymmetry—affect pooling configurations. We derive conditions that specify the optimal system flexibility configuration across these parameters. Our findings reveal that a partially flexible system can outperform a fully flexible one, particularly in asymmetric scenarios with low server capability. This advantage is also pronounced when considering a range of system costs, including server capability, cross-serving, and other related costs in stochastic service systems with and without buffers. These insights from our two-server model offer guidance on designing more efficient flexibility in complex multi-class, multi-server service systems.

Do Mutual Funds Respond to Mechanical Changes in ESG Ratings?

2026-01-13T00:00:00+00:00

Management Science
Using a quasi-experimental setting, we study whether mutual fund investors respond to a purely mechanical change in environmental, social, and governance (ESG) ratings—that is, a change independent of concurrent changes in firms’ actual ESG activities. We find that when a firm experiences a mechanical increase in ESG ratings, the probability of being selected by an ESG fund increases (extensive margin). In contrast, if the firm is already in the fund’s portfolio, its holdings do not change (intensive margin), consistent with portfolio weighting being based on market capitalization. The selection effect is observable not only among funds that follow an ESG index but also among active ESG funds, which presumably should have the resources and ability to identify and filter out the mechanical increase in ESG ratings. Among active ESG funds, the selection effect is stronger for funds with less assets under management, larger portfolios of firms, and lower expense ratios, consistent with the notion that resource constraints may impede a fund’s screening ability. Our findings imply that passive investing based on commercial ESG ratings—whether due to resource constraints or portfolio indexing—might result in portfolio allocations that do not reflect the actual ESG activities of firms.This paper was accepted by Suraj Srinivasan, accounting.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.06184 .

EXPRESS: The Impact of Figure-Ground Reversal (FGR) in Brand Logos on Brand Attitude

2026-01-13T00:00:00+00:00

Journal of Marketing Research
Figure-ground reversal (FGR) transcends visual conventions by reversing the roles of figure and ground in brand logo designs. In this research, the authors study how FGR logos affect consumers’ brand attitudes. Using traditional self-reported measures as well as biometric technology, they illuminate the unique nature of FGR’s underlying mechanism and identify moderators to shed additional light on that process. Specifically, they find that the positive effect of FGR logos on brand attitude is mediated by engagement and aesthetic appeal, and moderated by the visual identification and semantic interpretability of FGR objects. Across a multi-method investigation that includes live bidding, incentive-compatible willingness-to-pay, eye-tracking, and multiple boundary condition experiments, the authors provide empirical support for these effects and reveal the underlying mechanism. They conclude by discussing the contributions of the research to the literature on visual marketing phenomena and the implications of the findings for better visual branding in the marketplace.

EXPRESS: The Effect of Downvotes on Content Creation: Evidence from Social Media

2026-01-13T00:00:00+00:00

Journal of Marketing Research
This paper studies how receiving negative peer feedback, in the form of downvotes, affects UGC creation on Reddit. We focus on the following outcomes: (a) propensity to post (incidence), (b) where users post, and (c) the strength of opinion (intensity), measured by the extremity of users’ texts. The latter two outcomes are important given ongoing concerns about how social media platforms may contribute to echo chamber formation and polarization. We find that negative feedback increases users’ subsequent posting activity, relative to no feedback, and we do not find evidence that receiving negative feedback drives users away, alleviating concerns about echo chamber formation. In addition, negative peer feedback moderates extreme sentiments—when initial views are extreme, users temper the intensity of their subsequent posts. These effects of negative feedback are consistent with users attempting to maintain their reputation.

EXPRESS: From Owning to Connecting: Understanding and Leveraging the Effect of Internet Meme Marketing

2026-01-13T00:00:00+00:00

Journal of Marketing
Internet meme marketing is a digital marketing practice in which marketers leverage internet memes to promote their brand or product. Despite its growing adoption in brand communications, academic understanding of meme marketing remains in its infancy. In this research, we theoretically develop and empirically test how, why, and when meme marketing is effective. We find that meme marketing enhances digital advertising effectiveness by fostering shared psychological ownership of the marketing message and subsequently a strengthened self–brand connection. In addition, the effectiveness of meme marketing diminishes when the leveraged memes are not in their maturity (i.e., during the introduction, growth, or decline stages) or when applied to promote niche products. Six studies—including a large-scale secondary data analysis (N = 900,139 posts), a field experiment (N = 423,565 impressions), and four controlled experiments (N = 3,958 participants)—provide robust and converging evidence for these propositions. The effectiveness of meme marketing is demonstrated across both behavioral outcomes (e.g., likes, click-through rates, conversion rates) and attitudinal responses (e.g., purchase intention, likelihood to like). This research advances theoretical understanding of meme marketing and offers actionable insights for practitioners seeking to leverage internet memes in their brand digital marketing communications.

EXPRESS: Does Amazon’s Dual Role Weaken Marketplace Competition?

2026-01-13T00:00:00+00:00

Journal of Marketing
Amazon’s dual role, as both marketplace owner and first-party (1p) seller, gives it power over third-party (3p) sellers who sell similar items. This dual role can weaken 3p sellers’ ability to compete, possibly harming 3p sellers and consumers. We examine three aspects of marketplace competition. First, we examine price change dependencies. We find that 1p price drops after either higher Buy Box (i.e., the Add to Cart or default sales box on Amazon’s product page) prices or large 3p price increases; 3p prices decrease subsequently. Second, we analyze Buy Box seller selection since this is a critical conduit for demand. We find both high 1p and 3p prices are penalized in Buy Box selection. Low-reputation and intermittent 3p sellers cannot win Buy Box even at significantly lower prices. At equal prices, for some prices, Buy Box favors 1p over equal-priced 3p, and vice versa for others. Third, to see whether entry barriers weaken competition, we estimate a 3p seller entry model. Higher 1p prices are associated with more 3p sellers, suggesting low entry barriers. Combined, our results suggest Amazon’s dual role does not weaken competition in the marketplace. We discuss implications for marketplace participants, antitrust policy and research.

How Are Skills Changing with Digital Technologies? Clarifying Boundary Conditions in Management Research

2026-01-13T00:00:00+00:00

Journal of Management Studies
This article contributes to discussions about the future of work by providing a systematic review of the broad yet fragmented management literature on how skills are changing with digital technologies (DTs). Our aim was to understand the nature of scholarly engagement with this relationship to inform a future research agenda. Our systematic review identified 225 original empirical articles that explicitly examined skills and DTs. We highlight the diverse categories of context, mechanisms, and outcomes in the form of an analytical matrix, guided by an organizational conceptualization of skill that spans positivist and social constructivist approaches. Our analysis generates four theoretical framings of how skills are changing with DTs:market shift,control shift,recombination, andimbrication. These framings are derived from two analytical dimensions of skill change: transactional versus transformational and objective versus relational. We interrogate the fit between each theoretical framing and the affordances of DTs, bringing to light how skill change with DTs is entangled with shifting temporal dimensions and a rich variety of interactions and interdependencies. Our theoretical framings, analytical dimensions, and insights into the relationship between skill change and DTs provide the basis for a more coherent management research agenda.

Are Social Media Platforms a Threat to Democracy? An Ecosystem Governance Perspective

2026-01-13T00:00:00+00:00

Journal of Management Studies
Castello, Colleoni, Scherer, and Trittin contend that social media platforms threaten democratic processes by facilitating the spread of misinformation and fostering polarized debates – dynamics that ultimately serve to monetize user attention. We acknowledge the problem but challenge their proposed solution of conceptualizing social media as political spaces. Instead, drawing on ecosystem theory, we propose an ‘ecosystem failure’ analysis as a more robust analytical framework. Within this perspective, we see these information‐related problems as negative externalities that weaken the entire platform ecosystem. These externalities are structurally distinct due to algorithmic curation that selectively amplifies content in ways that reinforce user biases, strong network effects with almost zero replication costs, and engagement‐based monetization that decouples production from editorial standards. We therefore advance a reflexive ecosystem governance approach that accounts for broader ecosystem value, shifting the focus beyond short‐term economic performance to encompass overall ecosystem health. This approach transforms the management of negative externalities into a core strategic interest for the platform and aligns the incentives of individual actors ex ante, by design, prioritizing risk‐adjusted reach and demotion of low‐integrity content over ex post moderation. We analyse the mechanisms that align private incentives with democratic resilience and highlight the inherent trade‐offs involved.

Custodians at the Crossroads: Managing Change at Institutionally Significant Places

2026-01-13T00:00:00+00:00

Journal of Management
Custodians are integral to institutional maintenance, and instrumental to preserving institutionally significant places. Yet when institutional decline impacts institutionally significant places, we do not know how custodians manage the tensions between preservation efforts aimed at saving these places and the need for adaptation. Using a rich qualitative dataset of interviews with custodians from and participant observation at 26 rural church buildings in England, we examine custodian responses when institutional decline in belief in Christian religion impacts sacred church buildings. We find that in response to experiencing place materiality, relational, and practices tensions, custodians are torn between preserving the institutional role of place and the need to find resources to maintain such places. Custodians manage these tensions by deliberatively evaluating materiality alterations and adopting innovative practices within the bounds of institutional appropriateness and resource constraints. The process we flesh out leads to distinct outcomes: place augmentation, practices augmentation, and, in a few instances, no augmentation. Our model portrays the pathways followed by institutional custodians that are rooted in the tensions they experience between the attachment to the institutional role of place and the need for change in response to institutional decline. Our study contributes to research on custodianship and place by uncovering divergent custodian responses when institutional decline impacts institutionally significant places and place as an institutional carrier and the locus of custodian place work.

How Do Entrepreneurs Form Social Business Opportunity Beliefs? An Opportunity Actualization Perspective

2026-01-13T00:00:00+00:00

Entrepreneurship Theory and Practice
The opportunity actualization perspective has recently gained attention as a key framework for exploring entrepreneurial opportunity. The perspective’s theorizing, however, predominantly focuses on profit-driven opportunities, limiting its applicability across the spectrum of entrepreneurial behavior. To broaden applicability, we examine how entrepreneurs motivated by social and economic considerations form their opportunity beliefs. We use a qualitative research approach that combines think-aloud protocols and semi-structured interviews with 24 impact entrepreneurs. Our findings allow us to develop a holistic model of social business opportunity belief formation, incorporating market and non-economic considerations, emphasizing the role of non-market stakeholders. We derive important implications for both theory and practice.

Curious and analytical: How analysts evaluate and respond to executive communications about firm strategy

2026-01-02T00:00:00+00:00

Strategic Management Journal
How do analysts react to communication about firms' strategies? Research has shown that executive communication influences markets, but we know little about reactions to the deeper strategy content communicated. Drawing from research on how evaluative frames and expectation violations shape cognition, we show that when executives focus on growth strategies, analysts react by becoming more curious and analytically intensive. These changes in cognition partially mediate a positive effect on analysts' evaluations. Further, we consider two situations that reveal how analysts react to information that reinforces or violates their expectations, demonstrating different analyst reactions to similar strategies among S&P 500 firms. In doing so, we contribute new theory about how the evaluative assumptions of these influential market actors change and can be influenced by managerial framing.Managerial SummaryThis paper focuses on how securities analysts react to executive communications about their business' strategies. We know that top executive communications influence markets, but this paper probes further to understand reactions to the specific strategies that are communicated. We show that when executives focus on new growth strategies, this piques analyst interest. Specifically, we find that this makes analysts more curious and analytically intensive, thereby shaping their forecasts and evaluations. However, we find that analyst reactions also depend on how well the strategies that executives talk about fit with what is expected from the company. When the strategy articulated aligns with expectations, firms are rewarded with yet stronger evaluations. In contrast, when a strategy does not align with expectations, firms are punished with lower evaluations.

On the Expansion of Risk Pooling

2026-01-02T00:00:00+00:00

Management Science
Risk pooling has become an increasingly critical tool for managing risks among corporations, institutions, states, and nations, with examples including multinational pooling, decentralized insurance schemes, catastrophe risk pooling, and burden sharing for nuclear accidents. Although there has been a rich literature on such practices, little is known from a theoretical viewpoint regarding the operational strategies of risk pools and, in particular, on issues about the effect of a pool’s expansion on each existing member’s welfare. This paper is the first to explore these issues by establishing different notions of consensus for the expansion of a risk pool: strong consensus, where both existing members and new candidates improve their risk measures due to the pool’s expansion, and weak consensus, which refers to the willingness of existing members to remain in the pool. Under optimal risk sharing for each pool, we show that its properties regarding expansion’s effects depend strongly on the underlying pricing rule. For instance, only with risk-adjusted equilibrium pricing are existing members willing to accept highly risky new members, whereas simple linear pricing excludes such members from the pool. Additionally, the impact of exogenous reinsurance on consensus is analyzed under both pricing rules. The established framework offers valuable managerial insights and policy implications, which we illustrate through two indicative case studies based on real data.This paper was accepted by Agostino Capponi, finance.Funding: Financial support from the Tsinghua University School of Economics and Management [Research Grant 2023051001], the National Social Sciences Foundation of China [Major Grant 23&ZD178] is gratefully acknowledged, and the Tsinghua University Independent Research Grant [2024THZWYY02].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.06863 .

Does After‐Hours Telepressure Increase Next‐Day Emotional Exhaustion or Cognitive Flexibility? The Role of Different Work‐Related Rumination Processes

2026-01-02T00:00:00+00:00

Human Resource Management
The increasing use of information communication technology (ICT) has made telepressure, the urge to reply quickly to work‐related messages, a common experience among today's employees. By integrating theoretical underpinnings of conservation of resources with rumination literature, we posit that after‐hours telepressure leads to a resource‐loss process via evening affective rumination, as this type of rumination increases next‐morning emotional exhaustion. In the meantime, after‐hours telepressure enables a resource‐gain process via evening problem‐solving pondering, as this type of rumination promotes next‐morning cognitive flexibility. Moreover, we argue that leader path‐goal facilitation, leader behaviors that facilitate employees' goal achievement by providing guidance and removing obstacles, alleviates the impact of after‐hours telepressure on employees' next‐morning emotional exhaustion by reducing their evening affective rumination and strengthens the positive effect of after‐hours telepressure on employees' next‐morning cognitive flexibility by increasing their evening problem‐solving pondering. Through a 10‐day experience sampling investigation of 81 employees working in various industries in China (i.e., Study 1) and a 5‐day experience sampling investigation of 95 employees in an e‐commerce company in Singapore (i.e., Study 2), we find support for most of our hypotheses. As a whole, by providing a more balanced account of the consequences of after‐hours telepressure from the COR perspective, our work advances extant management literature and offers novel practical insights in terms that companies can help employees suffer less and even benefit from telepressure by enhancing leaders' levels of path‐goal facilitation.

Cultural Entrepreneurship and the Spirit of Japanese Capitalism

2026-01-02T00:00:00+00:00

Entrepreneurship Theory and Practice
While cultural entrepreneurship research has highlighted how entrepreneurs craft stories to legitimize their ventures, we know much less about how entrepreneurs gain legitimacy for a novel organizational form. Drawing upon a detailed, historical examination of Japanese family mottos from the Edo period (1603–1868), we show how stories embedded in family mottos enabled legitimation by symbolically leveraging two core socio-cultural institutions of Edo Japan (religion and family), advancing our understanding of how broader cultural resources can be leveraged to legitimate a new organizational form. We discuss implications for scholarship on family business and cultural entrepreneurship.

Entrepreneurial decision‐making under uncertainty and competing goals

2026-01-04T00:00:00+00:00

Strategic Entrepreneurship Journal
Entrepreneurs make critical decisions in uncertain environments where information is limited, outcomes are difficult to predict, and multiple goals often compete. Yet, existing research offers scattered insights into how entrepreneurs dynamically adapt to such contexts and how their decisions are shaped by behavioral and cognitive foundations such as judgment, intuition, and experience. We shed light on these phenomena by exploring how decision‐making is influenced by factors at multiple levels, from individual traits and family dynamics to team interactions and organizational structures. A key aspect of our inquiry focuses on how entrepreneurs manage uncertainty by balancing economic goals, such as growth and profitability, with non‐economic objectives like social impact, sustainability, or knowledge advancement. By integrating these perspectives, this work offers a conceptual framework that connects antecedents, processes, and outcomes of entrepreneurial decision‐making under uncertainty and competing goals, providing a promising roadmap for future research.Managerial SummaryEntrepreneurs often make decisions in uncertain environments, where they must contend with limited information and competing goals. This work explores how entrepreneurs balance economic objectives, such as profit, with non‐economic ones, like satisfying various stakeholders, achieving social impact, and sustainability. It highlights the role of individual, family, team, and organizational factors in shaping these decisions, offering novel insights into how entrepreneurs can manage trade‐offs, adapt feedback‐based strategies, and recalibrate priorities over time. For owners, managers, and business leaders, understanding these dynamics can lead to better decision‐making, improved risk management, enhanced strategic alignment, increased innovation, and a more balanced approach to growth.

Managers' Decisions About Informal Accommodation Requests by Employees With and Without Disabilities

2026-01-04T00:00:00+00:00

Human Resource Management
Although formal accommodations are required by law across many jurisdictions, many employees seek informal adjustments to their work conditions. These individualized work arrangements are not rooted in legal compliance but are instead provided at managers' discretion. Employees without disabilities routinely ask for changes to their work conditions (e.g., flexible work arrangements), and these changes often mirror those requested by employees with disabilities. Using Social Exchange Theory as our conceptual lens, we examined the critical role of managers' decision‐making on informal accommodation requests through three policy‐capturing studies. As hypothesized, managers were more likely to grant informal accommodations to employees with longer tenure, stronger task performance, and more citizenship behaviors. Moreover, tenure consistently had the strongest influence on accommodation intentions. Managers were also more likely to grant informal accommodations for disability reasons than for family‐related reasons, contrary to our expectations. Our research offers novel insights into how managers view informal accommodation requests from employees with and without disabilities. This study provides crucial theoretical contributions to the human resource management literature and informs practical considerations on issues faced by managers and organizations.

Bending Without Breaking: How Chinese Patent Examiners Navigate Value Tensions

2026-01-05T00:00:00+00:00

Organization Science
Professionals increasingly work within commercial and governmental organizations whose mandates often create tensions with their professional values. Prior research, however, shows mixed findings regarding how professionals respond to these value tensions, with some values being protected and others being compromised. We explore this inconsistency by investigating how Chinese patent examiners navigate tensions between their professional values and governmental pressures to promote domestic strategic innovation under the Made in China 2025 policy. Using a mixed-methods approach that combines qualitative interviews with 27 Chinese patent examiners and quantitative analyses of nearly 1 million patents reviewed by 10,304 examiners, we develop a hierarchical framework of professional values. We find that examiners perceive their values hierarchically, distinguishing between core identity-defining values and subordinate operational values. Under external pressures, they attempt to protect their core value of judgmental fairness while absorbing the pressures by compromising their subordinate value of diagnosing diligence. Professional interactions with foreign examiners and experienced patent agents mitigate this compromise by anchoring examiners’ attention to the operational details where diagnosing diligence is embedded. Moreover, our quantitative analyses reveal that compromised diagnosing diligence ultimately undermines judgmental fairness due to interdependence within the value hierarchy. Our study advances the understanding of professional value dynamics under external pressures and provides insights into how experts balance their professional ideals with institutional mandates.Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2024.18504 .

Revenue Maximization and Learning in Product Ranking

2026-01-05T00:00:00+00:00

Operations Research
The Price of Attention: Ranking Products for Maximum RevenueHow should an online retailer rank products when customers have limited attention spans? Chen, Li, and Yang tackle this classic problem by extending the well-known cascade model to account for two crucial, real-world factors: customers view only a random number of items, and the firm’s goal is to maximize revenue, not just clicks. This creates a difficult trade-off between ranking popular, low-price items and riskier, high-price ones. The authors propose the “Best-x” algorithm, an efficient method for finding a near-optimal ranking. They prove it guarantees a revenue of at least 1/e (approximately 37%) of that achievable by a clairvoyant who knows each customer’s attention span in advance. For cases where product attractiveness and attention distributions are unknown, the authors also devise the RankUCB online learning algorithm, which learns personalized rankings from customer interactions and achieves near-optimal performance over time.

Is Competition Only One Click Away? The Digital Markets Act’s Impact on Google Maps

2026-01-05T00:00:00+00:00

Marketing Science
European Union-mandated changes to Google results increased map-related searches but produced little traffic gain for competitors, underscoring Google Maps’ dominance.

The ESG-Innovation Disconnect: Evidence from Green Patenting

2026-01-05T00:00:00+00:00

Management Science
We document that traditional energy firms are key innovators in the United States’ green patent landscape. These firms produce more, and significantly higher-quality, green innovation. In many green technology spaces, they appear to be influential first movers and to produce ongoing foundational aspects of innovation and commercialization on which other alternative energy producers build. They additionally invest significantly in labor and capital to complement these green innovations. These traditional energy firms, however, receive significantly lower environmental, social, and governance (ESG) scores and fund flows and are not rewarded for incremental green innovation. This behavior is consistent with a competitive response by traditional energy firms to preempt obsolescence of current technology by investing in future replacement technologies.This paper was accepted by Bo Becker, finance.Funding: Funding was provided by the National Science Foundation [Grant SciSIP 1535813] and the Fordham University Gabelli School of Business—PVH Corp. Global Thought Leadership Grant on Corporate Social Responsibility.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.09124 .

Patience as a pathway to optimal consumer experience and behavior

2026-01-05T00:00:00+00:00

Journal of Consumer Psychology
Patience has been a consistent source of interest and inspiration for generations of thinkers and writers. In this article, I delineate the tenets of a comprehensive framework for understanding patience, theprocess model of patience(Sweeny,Personality and Social Psychology Review, 2025, 29, 145), that positions impatience as a common and unpleasant emotion and patience as a targeted form of emotion regulation. I then outline potential applications of the model for consumer decisions, service experiences, and marketing contexts and close with practical recommendations to optimize consumer behavior in contexts where impatience is likely to arise.

EXPRESS: Sequential Sponsored-Products and Off-Amazon Advertising Optimization for Etailers

2026-01-06T00:00:00+00:00

Production and Operations Management
Sponsored-Products (SP) advertising is a popular way to promote products on Amazon. Etailers who have a large catalog of products often create SP ad groups for products with similar attributes. An SP ad group consists of a set of products and a set of keywords, and all the products in the ad group share the same keyword set. These keywords are the ones that shoppers may search for when looking for products on Amazon. In addition to SP ads, etailers may link to external websites for advertising their products, which is called off-Amazon (OA) ads. This study focuses on the optimization of sequential SP and OA (abbreviated as SSPOA) ads decisions for etailers. Practically, many etailers set sales targets for products as manufacturing and logistics are planned ahead of time. Hence, we consider the etailer’s objective as minimizing the expected long-run average cost incurred by advertising and cumulative unmet sales target. We model the SSPOA optimization as a controlled Markovian multi-armed bandit (MAB) process. When the mean of the sales number per unit time (i.e., sales rate) for each product is known, we characterize the etailer’s optimal SSPOA policy for products in an ad group. In reality, etailers may not know the exact means of sales rates. To learn the unknown parameters while simultaneously minimizing the long-run average cost, we develop a Thompson-sampling-based algorithm for the controlled Markovian MAB problem that couples the SP and OA ads decisions. We prove that the regret bound of the proposed algorithm isO~(T), whereTis the total horizon length. Compared with existing literature, our problem additionally considers the regret from applying the estimated control policy and the impacts of choosing non-optimal keyword sets on subsequent states. We also conduct numerical experiments that validate our theoretical results. Moreover, we extend the base model in several directions, i.e., considering unknown transition rates between different sales rate levels, incorporating correlated keyword sets, and learning the optimal policy using Posterior Sampling for Reinforcement Learning under a discretized setting.

EXPRESS: Street Orders as an Outside Option: Incentives and Information Disclosure of Taxi-Hailing Platforms

2026-01-06T00:00:00+00:00

Production and Operations Management
Taxi drivers face tremendous market competition pressure in the private transport industry after independent contractor drivers (i.e., on-demand ride-hailing vehicles) entered the field, in the era of sharing economy. Taxi drivers have to go online and join platforms to secure demand. However, while ride-hailing drivers have platform orders as the only source of demand, taxi drivers can serve both platform andstreet orders(i.e., people waving on the roadside or waiting at taxi stands), and switch between the two on a real-time basis. The existence of street orders as anoutside optioncreates a decision problem for taxi drivers (i.e., which orders to pick) and forces platforms to carefullydesign incentives(e.g., commission and subsidy schemes) to attract taxi drivers in order to maximize profit. More interestingly, in common practices, platforms do not disclose trip destinations to ride-hailing drivers before customers get on board, to prevent cherry-picking behaviors (i.e., drivers only pick their preferred orders, for example, long-distance ones). However, many platforms provide trip information to taxi drivers in advance, as an incentive to compete against street order opportunities. Thus,taxi drivers need to choose between platform orders with trip information but also pick-up time (i.e., the unpaid driving duration to pick up customers) and street orders without trip information but zero pick-up time.This paper studies how a taxi driver should choose between platform and street orders, and how the platform should design incentives to attract taxi drivers in order to maximize profit. To this end, we build a game-theoretical model and derive optimal strategies. We find that it is optimal for the platform to allow drivers to serve both platform and street orders, rather than incentivizing exclusive service to platform. Since street orders exist as an outside option and protect drivers’ surplus, the platform’s profit will be limited if exclusivity is enforced. In addition, the disclosure of trip distance reduces the subsidy burden on the platform, compared to non-disclosure. Later in the paper, we also check the robustness of our findings under various settings, including homogeneous orders, zero commission and subsidy, distance-based subsidy scheme, and heterogeneous pick-up times.

EXPRESS: The Implications of Retail Trade-ins on Sales, Returns, and Profitability: An Empirical Analysis of a Jewelry Trade-in Program

2026-01-06T00:00:00+00:00

Production and Operations Management
Most retailers offer trade-in programs that allow customers not only to trade-in a used product in part payment for the purchase of a new one, but also to return the new product purchased via a trade-in. In this context, we study how a jewelry trade-in program affects sales and returns of trade-in eligible vs. ineligible new products, and how such programs can be better designed to improve profitability. We conduct an empirical analysis using data from a national jewelry retailer that increased the number of stores offering the trade-in program. Leveraging this expansion, we show that the trade-in program impacts sales and return rates, and find that this impact substantially differs across trade-in eligible vs. ineligible products. For trade-in eligible products, sales increase by 11.4%, and so do return rates - by 4.3 percentage points. For trade-in ineligible products, sales increase by 2.7% and there is no impact on return rates. The provision of a trade-in option reveals a novel trade-off for retailers: it leads to higher sales, but also results in greater returns, which can be detrimental to profitability. Retailers therefore need to carefully assess this trade-off to manage trade-in eligibility of different products. A counterfactual analysis suggests that a selective trade-in program accounting for this trade-off can enhance program profitability by 19%.

Platform Disintermediation: Information Effects and Pricing Remedies

2026-01-06T00:00:00+00:00

Operations Research
Platform Disintermediation: Information Effects and Pricing RemediesIn “Platform Disintermediation: Information Effects and Pricing Remedies,” S. Sekar and A. Siddiq analyze how platforms use pricing and informational levers to combat disintermediation, where sellers transact off-platform to avoid commission fees despite losing payment protection. The authors find that a platform facing a high threat of disintermediation may optimally raise its commission rates in a high-information environment, prioritizing revenue maximization from remaining secure transactions rather than preventing all off-platform transactions. Furthermore, they show that increasing sellers’ switching costs may actually reduce platform revenue by enabling sellers to pass the cost onto on-platform buyers via higher prices. The analysis also demonstrates that a platform can benefit from providing a partially informative signal about buyer riskiness, as full information reduces the value of the platform’s protection and strengthens the disintermediation incentive. Finally, whereas access-based pricing (upfront fees) prevents disintermediation, it may yield less revenue than commissions when seller quality is highly heterogeneous.

How Do Technology Paradigms Influence Configurations of Contract Characteristics for Success of Inter‐Organizational Outsourcing Projects, 1991–2009?

2026-01-06T00:00:00+00:00

Journal of Operations Management
What are the distinct configurations of contract characteristics associated with the success of inter‐organizational outsourcing projects across different technology paradigms? We examine information technology outsourcing contracts between 1991 and 2009 to address this question by using a relatively new approach based on qualitative comparative analysis. We consider four technology paradigms: pre‐Internet (1991–1996), pre‐Dotcom (1997–2000), post‐Dotcom (2001–2005), and Cloud Computing (2006–2009). We discuss issues related to adverse selection and moral hazard and identify five key contract characteristics that determine contract success: new contract, existing organizational relationship, long contract duration, fixed price, and competitive bidding. Our analyses document two key findings. First, we show that configurations of contract characteristics for success and failure of outsourcing projects are different across technology paradigms. Second, we identify three themes in configurations associated with outsourcing success—economic imperative, conservative relational, and conservative imperative. These themes extend prior work that draws on transaction cost economics, social exchange theory, and relational exchange theory and identify an increasing emphasis on the relational component to manage contracting risk for outsourcing success over time. From a managerial perspective, we provide context‐sensitive causal recipes to choose configurations of contract characteristics, considering technology paradigms. Together, our findings provide new insights for developing cumulative knowledge for understanding the determinants of success of interorganizational outsourcing projects while opening new avenues for further theorizing and empirical testing.

Default Risk and the Pricing of U.S. Sovereign Bonds

2026-01-06T00:00:00+00:00

The Journal of Finance
We examine the relative pricing of nominal Treasury bonds and Treasury inflation‐protected securities in the presence of U.S. default risk. Hedged breakeven inflation is significantly positively related to U.S. default risk, driven by correlation between shocks to default risk and both shocks to inflation swap premia and Treasury yields. To understand the mechanisms through which default risk is related to inflation swaps and sovereign yields, we estimate an affine term structure model to capture their joint dynamics. Our estimation implies that the interaction between inflation dynamics and default is the primary source of differential pricing.

The Elephant and Donkey in the Room: Time-Varying Effects of Political Dissimilarity on Social Interactions at Work During U.S. Elections

2026-01-07T00:00:00+00:00

Organization Science
Political polarization is recognized as a global risk. Although emerging studies on political dissimilarity at work highlight important implications for how employees behave and interact, findings are at times inconsistent. To provide a more nuanced understanding of when and why political dissimilarity disrupts workplace interactions, we draw on the social identity approach and threat processing to examine how political dissimilarity shapes perceptions of work relationships and behavior before and after election events. Across three studies, we demonstrate that political dissimilarity’s effects depend on political macro events and thus become temporally activated. Study 1, an experience sampling field study during the 2020 U.S. presidential election, showed no significant impact of political dissimilarity on negative interpersonal interactions before the election, but significance emerged on election day and persisted for six days after the election. In Study 2, an online experiment during the 2022 U.S. midterm elections, we found that actual political dissimilarity indirectly influenced negative interpersonal interactions via reduced social mindfulness after the election but not beforehand. Study 3, a longitudinal experiment over four weeks during the 2024 U.S. presidential election, replicated the election effect, demonstrating that these effects persisted for at least two weeks and were mediated by cognitive (i.e., perspective-taking) and affective (i.e., empathic concern) subdimensions of social mindfulness. Our findings highlight political orientation as a critical dimension of workplace dissimilarity. Although its impact may be subdued, it becomes pronounced during macro-political events, shaping workplace interactions in significant ways, with the political dissimilarity effects being more easily reactivated in the postelection phase.Funding: Data collection was partially supported by an Add-on Fellowship for Interdisciplinary Economics and Interdisciplinary Business Administration from the Joachim Herz Foundation, awarded to M. Reinwald. P. Bamberger’s involvement in this project was supported by a grant from the Henry Crown Institute of Business Research in Israel.Supplemental Material: The online appendices are available at https://doi.org/10.1287/orsc.2024.18538 .

How to grow new applications out of old research? Evidence from firm cumulative investments in deep learning

2026-01-08T00:00:00+00:00

Strategic Management Journal
Firm technological research has the potential to spawn multiple applications. Despite recognizing such potential, past literature disagrees on the process through which firms discover and grow new applications out of their past technological research. I examine this question in the context of deep learning, taking a question‐driven approach. Difference‐in‐difference analysis suggests that firms radically increased cumulative investments in past deep learning research upon signals indicating elevated application potential of deep learning. Furthermore, rather than investing in proprietary efforts, firms disclosed their cumulative development trajectories to engage external innovation efforts from which they learn and build. Grounded in these findings, I propose that the discovery and growth of new applications of past research entailsunfolding innovation interdependencewhich motivates firms to co‐evolve with external innovators.Managerial SummaryFirm technological research has the potential to spawn multiple applications. This article examines how firms cumulatively invest in their past technological research to grow new applications in the context of deep learning. Employing a difference‐in‐difference approach, analysis suggests that firms radically increased cumulative investments in deep learning after a shock elevating the application potential of their past deep‐learning research. Furthermore, firms publicly disclosed their cumulative development trajectories to attract innovation efforts from application sectors while actively learning from the attracted efforts to innovate further. These findings suggest that firms engaged, leveraged and co‐evolved with external innovation efforts to discover and grow new applications of their past research.

Changing Opportunity: Sociological Mechanisms Underlying Growing Class Gaps and Shrinking Race Gaps in Economic Mobility

2026-01-08T00:00:00+00:00

The Quarterly Journal of Economics
We show that intergenerational mobility changed rapidly by race and class in recent decades in the U.S. and study the causal mechanisms underlying those changes. Between the 1978 and 1992 birth cohorts, earnings increased for white children from high-income families relative to white children from low-income families, increasing earnings gaps by parental income (“class”) by 30%. Earnings increased for Black children at all parental income levels, reducing white-Black earnings gaps for children from low-income families by 30%. Class gaps grew and race gaps shrank similarly for non-monetary outcomes such as educational attainment, standardized test scores, and mortality rates. Using a quasi-experimental design, we show that the divergent trends in economic mobility were caused by differential changes in childhood environments, as proxied by parental employment rates, within local communities defined by race, class, and childhood county. Outcomes improve across birth cohorts for children who grow up in communities with increasing parental employment rates, with larger effects for children who move to such communities at younger ages. Children’s outcomes are most strongly related to the parental employment rates of peers they are more likely to interact with, such as those in their own birth cohort, suggesting that the relationship between children’s outcomes and parental employment rates is mediated by social interaction.

Dual Demands, Attention, and Organizational Learning: Spatial and Temporal Replication of Routines in Scaling Organizations

2026-01-08T00:00:00+00:00

Organization Science
The replication of routines is fundamental to knowledge transfer and retention in organizations. Because research on routine replication has historically been divided, proceeding within knowledge transfer (spatial replication) or knowledge retention (temporal replication), respectively, our understanding of how replicating routines in new organizational units (knowledge transfer) affects an organization’s capacity to maintain adherence to those routines over time at existing units (knowledge retention) remains limited. Drawing on the organizational learning and related evolutionary economics literature on routines as well as the multiple goals literature and using data on a Fortune 100 franchise chain being scaled in the United States with thousands of outlets opened over a period of 10 years, we examine whether and how knowledge transfer affects knowledge retention. Our primary thesis is that knowledge transfer and knowledge retention create competing demands for limited attention and therefore the need to allocate attention between them. We posit that this gives rise to a negative relationship between the spatial (knowledge transfer) and temporal (knowledge retention) replication of routines, although the effect can be mitigated by organizational learning from experience. We find robust empirical support for our propositions, pointing to important attention and learning mechanisms that shape the organizational capacity to simultaneously navigate knowledge transfer and retention demands, that is, replicate routines across both space and time.Funding: This work was supported by Project PID2024-160548NB-I00 funded by MICIU/AEI /10.13039/501100011033 and by FEDER, EU as well as by by Fundação para a Ciência e a Tecnologia [UID/00124/2025, UID/PRR/124/2025, Nova School of Business and Economics] and LISBOA2030 [DataLab2030 - LISBOA2030-FEDER 01314200].Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.16528 .

Roadmap or Compass? The Value of Prior Collaborative Experience in an Unfamiliar Task Environment

2026-01-08T00:00:00+00:00

Organization Science
When a temporary team faces an unfamiliar task environment, it should particularly benefit from including members who have collaborated before. Although several studies have made this prediction, it has not been supported empirically. We reconcile this discrepancy by distinguishing between prior collaborations in tightly versus loosely coupled roles, defined by the degree of interdependence among collaborators during prior work. Both forms of prior collaboration can help teammates communicate effectively, aiding team adaptation in new contexts. However, prior collaboration in tightly coupled roles also fosters shared routines that may create inertia and impede adaptation. As a result, we argue that the value of tightly coupled experience declines in unfamiliar environments, whereas the value of loosely coupled experience increases. We test these ideas using data from esports, where professional players with varying collaborative histories are randomly assigned to temporary teams. Unanticipated changes to the game exogenously alter teams’ familiarity with their task environments. Consistent with our theory, we find that tightly coupled collaborative experience enhances team performance in familiar task environments but degrades it in unfamiliar ones. Loosely coupled experience provides modest benefits in familiar environments but substantially enhances performance in unfamiliar ones. Overall, our findings suggest that when environments change, tightly coupled experience can act as a faulty roadmap—anchoring teams to outdated routines—whereas loosely coupled experience can serve as a compass that promotes coordination and adaptation.Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2024.19399 .

Corporate ESG Profiles and Investor Horizons

2026-01-08T00:00:00+00:00

The Journal of Finance
We find that long‐term institutional investors tilt their portfolios toward firms with better Environmental, Social, and Governance (ESG) profiles, in the cross sections of both institutional investor portfolios and the ownership of firms. We test whether several theoretically motivated mechanisms can explain this relationship. Our results that long‐term investors exhibit patience with firms around poor earnings announcements, but quickly sell portfolio firms after negative ES incidents, support the view that long‐ and short‐term investors evaluate information differently. Our evidence shows that limits‐to‐arbitrage play a role, as we find that investors' ESG tilt weakens following regulatory shocks that shorten their horizon.

The Assignment of Intellectual Property Rights and Innovation

2026-01-08T00:00:00+00:00

Journal of Accounting Research
We study how the assignment of intellectual property rights between inventors and their employers affects innovation. Incomplete contracting theories predict that stronger employer property rights reduce the threat that employee inventors hold up their employers, thereby affecting inventor and invention outcomes. We test these predictions using a U.S. appellate court ruling that shifted the assignment of property rights from inventors to their employers. Within‐employer‐year analyses demonstrate that affected inventors are less likely to retain patent rights, assign patents to new employers, or leave their current employer, all consistent with reduced inventor ability to hold up their employers. Due to the reduced possibility of hold‐up, affected inventors’ innovations are revealed more promptly when disclosed, draw from a broader set of prior patents, and spread more to subsequent patents. If affected inventors do leave their employer, they are more likely to relocate to unaffected states. Furthermore, employers affected by the ruling are more likely to locate their inventors in agglomeration economies and alter their innovation strategy by reallocating activity across states and expanding their innovation portfolios. Our collective evidence suggests that shifting intellectual property rights to employers affects inventor and invention outcomes by reducing the threat of employee hold‐up from the employer's perspective.

Unequal in the Spotlight: Gender Differences in How Serving on Prominent Firms Affects Directors’ New Board Appointments

2026-01-08T00:00:00+00:00

Administrative Science Quarterly
Does gaining a foothold in the upper echelons of the corporate landscape carry different implications for women and men? We address this question by examining gender differences in how serving on the boards of prominent firms leads to new board appointments. While prominent affiliations are widely recognized as advantageous, research has yet to ask whether these benefits vary by gender. Using data on the population of directors in the FTSE-100 between 2010 and 2017, we find that women are, on average, more likely than men to obtain additional board appointments, consistent with prior literature showing that pressures to increase diversity stimulate demand for incumbent women relative to men. However, serving on more-prominent boards within the FTSE-100 increases men’s likelihood of obtaining new appointments but decreases it for women. Thus, women’s advantage diminishes and eventually reverses, becoming a disadvantage, as firm prominence increases. Our systematic evaluation of potential demand- and supply-side explanations for this pattern finds limited support for both. We propose, instead, that women’s experiences of greater scrutiny and of informal demands on more-prominent boards may shape their willingness to pursue additional appointments. We highlight the dual role of prominent affiliations as sources of both opportunity and constraint, with implications for individual careers and organizational diversity.

The Value of Silence: The Effect of UMG’s Licensing Dispute with TikTok on Music Demand

2026-01-09T00:00:00+00:00

Marketing Science
This study examines how the dispute between TikTok and Universal Music Group (UMG) impacted music demand on streaming platforms.

A Tale of Two Banks: When Credit Loss Models Meet Economic Crises

2026-01-09T00:00:00+00:00

Journal of Accounting Research
Policy makers and researchers are concerned that the expected credit loss (ECL) approach may exacerbate procyclicality. Using administrative loan‐level and firm‐level data in China, we find that banks adopting the ECL model reduced their credit supply and became more prudent in lending decisions after the onset of the COVID‐19 pandemic, compared to banks using the incurred credit loss (ICL) approach. Our findings are more pronounced for banks that experienced greater loan loss provisions induced by ECL and for firms with higher credit risk. The credit contraction persisted throughout our sample period. We further document that firms more exposed to ECL banks experienced larger reductions in loans, assets, liabilities, and revenue after the pandemic began than those more exposed to ICL banks. These findings support the conjecture that the ECL approach may exacerbate procyclicality.

Leveraging Employees' Social Capital for Organizational Resilience in Small and Medium‐Sized Enterprises: The Role of High‐Involvement Work Practices

2026-01-09T00:00:00+00:00

Human Resource Management
Employees' bridging social capital (EBSC), conceptualized as the collective bridging social capital that employees bring into the organization, has been recognized as a potential resource for fostering organizational resilience (i.e., the ability to survive and thrive when confronted with unexpected disruptions and challenges), especially for small and medium‐sized enterprises (SMEs) operating in a turbulent business environment. However, the questions of how and when EBSC relates to organizational resilience remain underexplored. Drawing on dynamic capabilities theory, we propose that knowledge sharing—a dynamic and emergent process where knowledge is introduced, exchanged, combined, and integrated within organizations—represents a key process through which EBSC may be associated with organizational resilience. We further propose that this mechanism is stronger in organizations that extensively implement high‐involvement work practices (HIWPs). Using data from 1131 participants (including top management team members, middle‐level managers, and entry‐level employees) across 175 SMEs in Nigeria, we find that the relationship between EBSC and knowledge sharing, as well as the indirect association between EBSC and organizational resilience via knowledge sharing, is stronger when HIWPs are high rather than low. These findings highlight the importance of HIWPs in leveraging EBSC to enhance organizational resilience, providing practical insights for SMEs seeking to harness EBSC for organizational advantages.